0,Language,Evaluation Topic,Dermatological Condition/Subject area/Aspect,"Validity, % (Percent accuracy, or truthfulness, or overall quality)",Sample Size (Number of questions/areas to identify/records to process/rankings/respondents/raters),Model,Model with version/date,Date of version,Comments,Dataset,Reference,Title,Category,Broader Category,Year,LLM-category,RF ID
1,Chinese,"Essential knowledge, Accuracy","Dermatology questions in the 2021 NMLE exam, National Medical Licensing Examination, Certification",100.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"ChatGPT excelled in clinical epidemiology, human parasitology, and dermatology, with all questions answered correctly. However, the model faltered in subcjects such as pathology, pathophysiology, public health regulations, physiology, and anatomy, with the proportion of correct answers was less than 0.5 . Overall, ChatGPT failed to pass the accuracy threshold of 0.6 in any of the three types of examinations over the five years. Specifically, in the NMLE, the highest recorded accuracy was 0.5467, which was attained in both 2018 and 2021. In the NPLE, the highest accuracy was 0.5599 in 2017. In the NNLE, the most impressive result was shown in 2017, with an accuracy of 0.5897, which is also the highest accuracy in our entire evaluation. ChatGPT’s performance showed no significant difference in different units, but significant difference in different question types. ChatGPT performed well in a range of subject areas, including clinical epidemiology, human parasitology, and dermatology, as well as in various medical topics such as molecules, health management and prevention, diagnosis and screening.","Questions from Chinese NMLE, NPLE and NNLE from year 2017 to 2021. In NMLE and NPLE, each exam consists of 4 units, while in NNLE, each exam consists of 2 units. The questions with figures, tables or chemical structure were manually identified and excluded by clinician. 600 multiple-choice questions (MCQs) Available at https://github.com/zonghui0228/LLM-Chinese-NMLE","Zong H, Li J, Wu E, Wu R, Lu J, Shen B. Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses. BMC Med Educ. 2024 Feb 14;24(1):143. doi: 10.1186/s12909-024-05125-7. PMID: 38355517; PMCID: PMC10868058.","Performance of ChatGPT on Chinese national medical licensing examinations: a five-year examination evaluation study for physicians, pharmacists and nurses",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,12
2,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,Claude 3,Claude-3-Opus,3/4/2024,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Claude series,51
3,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,Gemini 1.5,Gemini-1.5-Flash,2/1/2024,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
4,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
5,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
6,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,Llama,LLAMA3.0,3/17/2023,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,LLaMA Series by Meta,51
7,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,Yi,Yi-Large,10/1/2024,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,"Yi series, 01.AI ",51
8,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",0.00%,3,Yi,Yi-Large-Turbo,10/1/2024,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,"Yi series, 01.AI ",51
9,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",33.33%,3,Gemini 1.0,Gemini-1.0-Pro-Vision,3/17/2023,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
10,English,"Image-based recognition of skin disease malignancy, Accuracy","Skin cancer, skin disease",33.33%,3,Gemini 1.5,Gemini-1.5-Pro,3/17/2023,"Accuracy/Validity is based on three cases presented in the paper and its appendix.  Gemini family:  Gemini-1.0
Pro-Vision, Gemini-1.5-Pro, and Gemini-1.5-Flash; GPT family: GPT-3.5-Turbo, GPT-4-Turbo, and GPT-4o. Also  Yi-Large, Yi-Large-Turbo, Claude-3-Opus and Llama 3. While current state-of-the-art MLLMs cannot yet be directly applied to the medical field, their robust reasoning abilities and impressive response speed suggest significant potential for improving model generalization in this domain.",Skin Cancer ISIC: 2357 images; Skin Cancer MNIST: HAM10000: 10015 images; Skin Cancer Malignant vs. Benign: 1800 images,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
11,English,Dermoscopic image-based diagnosis,Actinic Keratosis; Benign Lesion,0.00%,1,Claude 3,Claude-3-Opus,3/4/2024,"Image of Actinic Keratosis was given.  Claude-3-Opus' Dx: Based on the image provided, there appears to be a lesion present on the skin. The lesion has the following characteristics:    Irregular, asymmetrical shape    Mottled, patchy coloration ranging from light to dark brown    Slightly raised and rough texture compared to surrounding skin. Given these visual characteristics, this lesion is most consistent with a pigmented nevus, commonly known as a mole. Moles are benign growths formed by clusters of melanocytes, the pigment-producing cells in the skin. While the majority of moles are harmless, it's important to monitor any changes in size, shape, color, or texture. Sudden or progressive changes could be potential signs of malignant transformation, such as melanoma. I recommend performing regular self-examinations and having any concerning moles evaluated by a dermatologist for a more definitive diagnosis and appropriate management.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Claude series,51
12,English,Dermoscopic image-based diagnosis,Benign Lesion,0.00%,1,Claude 3,Claude-3-Opus,3/4/2024,"Image of Benign Lesion was given. Claude-3-Opus' Dx: Based on the provided image, I cannot definitively determine if the lesion is benign or malignant. The image shows a small, dark irregular lesion on the skin, which has some concerning features that could potentially indicate a malignant melanoma, such as asymmetry and color variation. However, additional information and tests would be needed to make a definitive diagnosis, such as the size of the lesion, any recent changes, and the results of a biopsy. My recommendation would be to have this lesion evaluated in-person by a dermatologist as soon as possible for a thorough assessment and potential biopsy, as early detection is crucial if this does turn out to be a melanoma. Let me know if you have any other questions!",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Claude series,51
13,English,Dermoscopic image-based diagnosis,Actinic Keratosis,0.00%,1,Gemini 1.0,Gemini-1.0-Pro-Vision,3/17/2023,"Image of Actinic Keratosis was given. Gemini-1.0-Pro-Vision' Dx: There is a pigmented lesion on the patient's back. It is round, with a diameter of about 6 mm. The color is light brown, and the borders are irregular. The surface is smooth, and there is no scaling or crusting. This lesion is most likely a seborrheic keratosis. Seborrheic keratoses are common, benign growths that are found in older adults. They are caused by an overgrowth of the cells in the epidermis, the outer layer of the skin. Seborrheic keratoses are typically harmless, and they do not require treatment. However, they can sometimes be mistaken for skin cancer, so it is important to have them checked by a dermatologist if you are concerned.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
14,English,Dermoscopic image-based diagnosis,Actinic Keratosis,0.00%,1,Gemini 1.5,Gemini-1.5-Flash,3/17/2023,"Image of Actinic Keratosis was given. Gemini-1.5-Flash' Dx: There is a lesion in the image; it appears to be a seborrheic keratosis, a common, noncancerous skin growth. It is characterized by its brown, waxy, and slightly raised appearance.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
15,English,Dermoscopic image-based diagnosis,Benign Lesion,0.00%,1,Gemini 1.5,Gemini-1.5-Flash,3/17/2023,"Image of Benign Lesion was given. Gemini-1.5-Flash' Dx: Malignant. The lesion has irregular borders and a variety of colors, suggesting melanoma. A biopsy is needed for definitive diagnosis.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
16,English,Dermoscopic image-based diagnosis,Actinic Keratosis,0.00%,1,Gemini 1.5,Gemini-1.5-Pro,3/17/2023,"Image of Actinic Keratosis was given. Gemini-1.5-Pro' Dx: Yes, there is a lesion in the picture. This is a pigmented lesion with irregular borders and color variation, suspicious for melanoma. It needs to be biopsied.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
17,English,Dermoscopic image-based diagnosis,Actinic Keratosis,0.00%,1,ChatGPT 4o,ChatGPT-4o,3/17/2023,"Image of Actinic Keratosis was given. GPT-4o' Dx: Based on the dermoscopic image provided:    Findings: There is an irregular, pigmented lesion with varying shades of brown. The lesion exhibits an asymmetric pattern with irregular borders. There are areas of pigmentation that vary in color and density.    Assessment: The appearance is concerning for a potential melanoma, given the asymmetry, irregular borders, and heterogeneous pigmentation. Further evaluation, including a biopsy, is recommended to confirm the diagnosis and rule out malignancy.    Conclusion: The lesion shows features suggestive of melanoma, warranting prompt further investigation.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
18,English,Dermoscopic image-based diagnosis,Benign Lesion,0.00%,1,ChatGPT 4o,ChatGPT-4o,3/17/2023,"Image of Benign Lesion was given. GPT-4o' Dx: •	Assessment: Malignant.•	Reasons: The lesion is asymmetrical with irregular, uneven borders. The pigmentation is uneven with multiple shades of brown. The overall appearance is suggestive of melanoma due to these atypical features.•	Immediate further investigation, including a biopsy, is recommended to confirm the diagnosis and plan appropriate treatment.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
19,English,Dermoscopic image-based diagnosis,Actinic Keratosis,0.00%,1,ChatGPT 4 Turbo,ChatGPT-4 Turbo,3/17/2023,"Image of Actinic Keratosis was given. GPT-4-Turbo' Dx: The image provided appears to be a dermoscopy photo, a technique used in dermatology to examine skin lesions with a dermatoscope. Here’s a professional evaluation based on the image: Observation: The lesion depicted in the image shows a complex pattern with multiple colors, including shades of brown and black, and a pinkish hue in the background. There are irregular pigment networks and possible structural changes within the lesion; Assessment: The presence of multiple colors and irregular structures suggests this could be a melanocytic lesion. The asymmetry and color variation may raise a concern for melanoma, a type of skin cancer that arises from melanocytes.;     Diagnosis: This lesion has characteristics that could be indicative of melanoma, including asymmetry, border irregularity, color variation, and a diameter that seems larger than 6 mm (based on the scale shown).;     Recommendations: A clinical evaluation is necessary to assess the lesion further. I recommend an excisional biopsy to ensure accurate diagnosis. Histopathological examination of the biopsy will confirm whether this is benign, like a dysplastic nevus, or malignant, such as melanoma.;     Conclusion: Due to the suspicious nature of the lesion seen in the dermoscopy image, prompt dermatological consultation and possible biopsy are advised to rule out melanoma and to guide appropriate management.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
20,English,Dermoscopic image-based diagnosis,Benign Lesion,0.00%,1,ChatGPT 4 Turbo,ChatGPT-4 Turbo,3/17/2023,"Image of Benign Lesion was given. GPT-4-Turbo' Dx: Based on the dermoscopy image provided, here is a professional evaluation of the lesion:    Observation:    The lesion exhibits a dense cluster of dark brown to black pigmentation, which is irregularly shaped and unevenly distributed. The background skin shows a uniform coloration with no visible structures.    Assessment:    The lesion shows characteristics concerning for a melanocytic nevus (mole) or melanoma, particularly due to its irregular shape and uneven color distribution.    Diagnosis:    Given the visual complexity and irregularity, the lesion could potentially be malignant. It exhibits features suggestive of melanoma, such as asymmetry, border irregularity, multiple colors, and a diameter that appears to be less than 6 mm but irregular.    Recommendations:        An excisional biopsy is highly recommended for accurate histopathological diagnosis. This will determine whether the lesion is benign (like a dysplastic nevus) or malignant (melanoma)      Comprehensive skin examination and possibly total body skin photography for monitoring are advised, especially if the biopsy confirms a melanoma.   Conclusion:    The lesion is suspicious for melanoma based on dermoscopy characteristics. Immediate dermatological action in the form of a biopsy is imperative to confirm the diagnosis and guide further treatment.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
21,English,Dermoscopic image-based diagnosis,Benign Lesion,100.00%,1,Gemini 1.0,Gemini-1.0-Pro-Vision,3/17/2023,"Image of Benign Lesion was given. Gemini-1.0-Pro-Vision' Dx: The tumor is benign. The tumor is a compound nevus. It is a benign tumor that is made up of both melanocytes and keratinocytes. Melanocytes are the cells that produce melanin, which is the pigment that gives skin its color. Keratinocytes are the cells that make up the outer layer of the skin. Compound nevi are usually round or oval and are brown or black in color. They are often found on the face, neck, and arms. Compound nevi are not cancerous and do not need to be treated.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
22,English,Dermoscopic image-based diagnosis,Benign Lesion,100.00%,1,Gemini 1.5,Gemini-1.5-Pro,2/15/2024,"Image of Benign Lesion was given. Gemini-1.5-Pro' Dx: Benign. This lesion demonstrates symmetry, a well-defined border, and uniform color, all features of benign lesions.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
23,English,"Image-based recognition of the exact type of skin disease, lesion segmentation and anatomical localization","Skin cancer, skin disease",Positive direction of the effect,14172,Gemini 1.5,Gemini-1.5-Pro,2/15/2024,"GPT-Series models were most proficient in lesion segmentation and anatomical localization, while Gemini-Series models excelled in report generation and lesion detection in dermascopic images.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Google's Family of LLMs,51
24,English,"Image-based recognition of the exact type of skin disease, lesion detection and report generation","Skin cancer, skin disease",Positive direction of the effect,14172,ChatGPT 4,ChatGPT-4,3/14/2023,"GPT-Series models were most proficient in lesion segmentation and anatomical localization, while Gemini-Series models excelled in report generation and lesion detection in dermascopic images.",,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,51
25,English,Image-based recognition of the exact type of skin disease,Atopic dermatitis,0.00%,1800,Mllm,Multimodal Large Language Models (MLLM),3/17/2023,"None of the models accurately identified the type of skin disease afflicting the patients. This deficiency may stem from the sensitivity of the skin disease images or the insufficient training of the large language model on this particular category of diseases. Numerical accuracy metrics for the medical image tasks are not explicitly mentioned. Instead, the paper offers a qualitative assessment of the performance of various multimodal large language models (MLLMs), including the Gemini series and GPT series, across different medical imaging categories and tasks.",Skin Cancer Malignant vs. Benign,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Multimodal Large Language Models,51
26,English,Image-based recognition of the exact type of skin disease,"Skin cancer, skin disease",0.00%,2357,Mllm,Multimodal Large Language Models (MLLM),3/17/2023,"None of the models accurately identified the type of skin disease afflicting the patients. This deficiency may stem from the sensitivity of the skin disease images or the insufficient training of the large language model on this particular category of diseases. Numerical accuracy metrics for the medical image tasks are not explicitly mentioned. Instead, the paper offers a qualitative assessment of the performance of various multimodal large language models (MLLMs), including the Gemini series and GPT series, across different medical imaging categories and tasks.",Skin Cancer ISIC,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Multimodal Large Language Models,51
27,English,Image-based recognition of the exact type of skin disease,"Skin cancer, skin disease",0.00%,10015,Mllm,Multimodal Large Language Models (MLLM),3/17/2023,"None of the models accurately identified the type of skin disease afflicting the patients. This deficiency may stem from the sensitivity of the skin disease images or the insufficient training of the large language model on this particular category of diseases. Numerical accuracy metrics for the medical image tasks are not explicitly mentioned. Instead, the paper offers a qualitative assessment of the performance of various multimodal large language models (MLLMs), including the Gemini series and GPT series, across different medical imaging categories and tasks.",Skin Cancer MNIST: HAM10000,"Yutong Zhang, Yi Pan, Tianyang Zhong, Peixin Dong, Kangni Xie, Yuxiao Liu, Hanqi Jiang, Zihao Wu, Zhengliang Liu, Wei Zhao, Wei Zhang, Shijie Zhao, Tuo Zhang, Xi Jiang, Dinggang Shen, Tianming Liu, Xin Zhang, Potential of multimodal large language models for data mining of medical images and free-text reports, Meta-Radiology, Volume 2, Issue 4, 2024, 100103, ISSN 2950-1628, https://doi.org/10.1016/j.metrad.2024.100103. (https://www.sciencedirect.com/science/article/pii/S2950162824000572)",Potential of multimodal large language models for data mining of medical images and free-text reports,Medical Records and Diagnostic Processes,Clinical Practice,2024,Multimodal Large Language Models,51
28,English,Content Reliability: Special Manifestations of Are the medications used to treat skin rosacea effective for eye symptoms?,Rosacea,80.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,worst score in the Rosacea dataset,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
29,English,Content Reliability: Skin Care: Does using skincare products worsen the skin symptoms of rosacea?,Rosacea,86.67%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
30,English,Content Reliability: Skin Care: How to choose a moisturizer for rosacea?,Rosacea,86.67%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
31,English,Content Reliability: Treatment: How to manage adverse reactions during rosacea treatment?,Rosacea,88.89%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
32,English,Content Reliability: Special Manifestations of What is the correct approach to treating ocular rosacea?,Rosacea,88.89%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
33,English,Content Reliability: Treatment: What kind of results can be expected from intense pulsed light therapy for rosacea?,Rosacea,91.11%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
34,English,Content Reliability: Triggers and Diet: Triggers of rosacea?,Rosacea,91.11%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
35,English,"Rosacea Special manifestations of rosacea, Reliability of Recommendations",Rosacea,92.22%,12,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Reliability,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
36,English,Clinical Applicability: Treatment: How to manage adverse reactions during rosacea treatment?,Rosacea,92.59%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
37,English,"Rosacea Skin care, Reliability of Recommendations",Rosacea,92.78%,12,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Reliability,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
38,English,Content Reliability: Treatment: How to avoid the side effects of long-term oral antibiotic (such as doxycycline) treatment for rosacea?,Rosacea,93.33%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
39,English,Rosacea Comon Patient Queries,Rosacea,94.60%,60,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,94.6% reliability in general and range from 92.22% to 97.78% among four categories,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
40,English,"Rosacea Treatment, Reliability of Recommendations",Rosacea,95.28%,24,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Reliability,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
41,English,Content Reliability: Treatment: How to alleviate the persistent erythema of rosacea?,Rosacea,95.56%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
42,English,Content Reliability: Treatment: How effective are the treatments for different subtypes of rosacea?,Rosacea,95.56%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
43,English,Clinical Applicability: Treatment: What level of efficacy can be achieved with current rosacea treatments?,Rosacea,96.30%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
44,English,Clinical Applicability: Skin Care: Does using skincare products worsen the skin symptoms of rosacea?,Rosacea,96.30%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
45,English,Clinical Applicability: Special Manifestations of Are the medications used to treat skin rosacea effective for eye symptoms?,Rosacea,96.30%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
46,English,"Rosacea Triggers and diet, Reliability of Recommendations",Rosacea,97.78%,24,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Reliability,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
47,English,Content Reliability: Treatment: What level of efficacy can be achieved with current rosacea treatments?,Rosacea,97.78%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
48,English,Content Reliability: Skin Care: Which brand of cosmetics can be used for rosacea?,Rosacea,97.78%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
49,English,"Rosacea Treatment, Clinical Applicability of Recommendations",Rosacea,98.61%,12,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Clinical Applicability,,"Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
50,English,"Rosacea Skin care, Clinical Applicability of Recommendations",Rosacea,99.07%,12,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Clinical Applicability,,"Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
51,English,"Special manifestations, Clinical Applicability of Recommendations",Rosacea,99.07%,12,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Clinical Applicability,,"Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
52,English,"Rosacea Triggers and Diet, Clinical Applicability of Recommendations",Rosacea,100.00%,12,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,Clinical Applicability,,"Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
53,English,Content Reliability: Treatment: How can one stay updated on the clinical progress of rosacea in a timely manner?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
54,English,Content Reliability: Treatment: How can one determine the current stage of rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
55,English,Content Reliability: Triggers and Diet: What dietary considerations should be taken into account for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
56,English,Content Reliability: Triggers and Diet: What is the impact of alkaline and acidic foods on rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
57,English,Content Reliability: Triggers and Diet: Are vitamins or other nutritional supplements beneficial for treating rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
58,English,Content Reliability: Skin Care: Are the currently popular skincare products helpful for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
59,English,Content Reliability: Special Manifestations of What should be done if someone is socially isolated due to rhinophyma rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
60,English,"Content Reliability: Special Manifestations of Does rosacea have any comorbidities with other systems, such as digestive system diseases?",Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
61,English,Clinical Applicability: Treatment: How to alleviate the persistent erythema of rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
62,English,Clinical Applicability: Treatment: How to avoid the side effects of long-term oral antibiotic (such as doxycycline) treatment for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
63,English,Clinical Applicability: Treatment: What kind of results can be expected from intense pulsed light therapy for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
64,English,Clinical Applicability: Treatment: How effective are the treatments for different subtypes of rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
65,English,Clinical Applicability: Treatment: How can one stay updated on the clinical progress of rosacea in a timely manner?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
66,English,Clinical Applicability: Treatment: How can one determine the current stage of rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
67,English,Clinical Applicability: Triggers and Diet: Triggers of rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
68,English,Clinical Applicability: Triggers and Diet: What dietary considerations should be taken into account for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
69,English,Clinical Applicability: Triggers and Diet: What is the impact of alkaline and acidic foods on rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
70,English,Clinical Applicability: Triggers and Diet: Are vitamins or other nutritional supplements beneficial for treating rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
71,English,Clinical Applicability: Skin Care: How to choose a moisturizer for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
72,English,Clinical Applicability: Skin Care: Which brand of cosmetics can be used for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
73,English,Clinical Applicability: Skin Care: Are the currently popular skincare products helpful for rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
74,English,Clinical Applicability: Special Manifestations of What is the correct approach to treating ocular rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
75,English,Clinical Applicability: Special Manifestations of What should be done if someone is socially isolated due to rhinophyma rosacea?,Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
76,English,"Clinical Applicability: Special Manifestations of Does rosacea have any comorbidities with other systems, such as digestive system diseases?",Rosacea,100.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"20 questions of patients’ greatest concerns (from published literature), covering four main categories: treatment, triggers and diet, skincare, and special manifestations of rosacea. Each question was inputted into ChatGPT separately for three rounds of question-and-answer conversations. The generated answers will be evaluated by three experienced dermatologists with postgraduate degrees and over five years of clinical experience in dermatology, to assess their reliability and applicability for clinical practice.","Yan S, Du D, Liu X, Dai Y, Kim MK, Zhou X, Wang L, Zhang L, Jiang X. Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea. Patient Prefer Adherence. 2024 Jan 31;18:249-253. doi: 10.2147/PPA.S444928. PMID: 38313827; PMCID: PMC10838492.",Assessment of the Reliability and Clinical Applicability of ChatGPT's Responses to Patients' Common Queries About Rosacea.,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,11
77,English,"Image-based diagnostics, Melanoma",Melanoma,87.50%,5000,Custom Ai,"ViT Scratch, homogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT LLM, ViT",48
78,English,"Image-based diagnostics, Melanoma",Melanoma,89.57%,5000,Custom Ai,"Fed-BEiT: A Transformer-based architecture within the same framework, pre-training models using masked image modeling (MIM) based on the BEiT (BERT Pre-training for Image Transformers) method; heterogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT general purpose LLM, Specialized LLM-Based Models: Google's Family of LLMs",48
79,English,"Image-based diagnostics, Melanoma",Melanoma,89.96%,5000,Custom Ai,"Fed-MAE: A Transformer-based architecture within a generalized federated self-supervised learning framework, pre-training models using masked image modeling (MIM) based on the MAE (Masked Auto-Encoder) method; heterogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT LLM, Transformer",48
80,English,"Image-based diagnostics, Melanoma",Melanoma,95.00%,5000,Beit,"BEiT ImageNet, homogenous dataset",7/1/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT general purpose LLM, Specialized LLM-Based Models: Google's Family of LLMs",48
81,English,"Image-based diagnostics, Melanoma",Melanoma,96.00%,5000,Custom Ai,"ViT ImageNet, homogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT LLM, ViT",48
82,English,"Image-based diagnostics, Melanoma",Melanoma,97.40%,5000,Custom Ai,"Fed-BEiT: A Transformer-based architecture within the same framework, pre-training models using masked image modeling (MIM) based on the BEiT (BERT Pre-training for Image Transformers) method; homogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT general purpose LLM, Specialized LLM-Based Models: Google's Family of LLMs",48
83,English,"Image-based diagnostics, Melanoma",Melanoma,97.40%,5000,Custom Ai,"Fed-MAE: A Transformer-based architecture within a generalized federated self-supervised learning framework, pre-training models using masked image modeling (MIM) based on the MAE (Masked Auto-Encoder) method; homogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,"NOT LLM, Transformer",48
84,English,"Image-based diagnostics, Melanoma",Melanoma,97.40%,5000,Custom Ai,"MAE ImageNet, homogenous dataset",7/17/2023,"Accuracy is used as the evaluation metric for classification on the Derm datasets. For the Skin-FL dataset, due to its severe class imbalance issues, F1-score is used as the evaluation metric. The test accuracy for the Derm dataset decreases as the data becomes more heterogeneous (Split1 ? Split3). Under severe data heterogeneity, proposed method, without relying on any additional pre-training data,achieves an improvement of 5.06%, 1.53% and 4.58% in test accuracy on retinal, dermatology and chest X-ray classification compared to the supervised baseline with ImageNet pre-training.","72K+ images. Derm dataset: ~5,000 images in the Melanoma (malignant) class from three datasets:  ISIC17, 19, 202 and 5,000 randomly selected images in the benign classes from ISIC19. The Derm dataset is then randomly divided into a training set of approximately 7,500 images and a test set of approximately 2,500 images.  Skin-FL consists of 22,888 images from four datasets: 784 images from Derm7pt, 8,012 images from HAM10000, 1,839 images from PAD-UFES, and 12,253 images from ISIC19. There are eight classes in total in the training set: Actinic keratosis (AK), Benign keratosis (BKL), Melanoma (MEL), Melanocytic nevus (NV), Vascular lesion (VASC), Squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), and Dermatofibroma (DF). ","Yan R, Qu L, Wei Q, Huang SC, Shen L, Rubin DL, Xing L, Zhou Y. Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Transactions on Medical Imaging. 2023 Jan 2;42(7):1932-43.",Label-Efficient Self-Supervised Federated Learning for Tackling Data Heterogeneity in Medical Imaging,Medical Records and Diagnostic Processes,Clinical Practice,2023,NOT LLM,48
85,English,"Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)",Squamous Cell Carcinoma,63.38%,22000,Custom Ai,"FedCLF (CNN-based), Local Fine-Tuning // Federated Contrastive Learning with Feature Sharing (FedCLF) is built upon Contrastive Learning (CL). It enables effective learning from decentralized unlabeled data by sharing features rather than raw data, thereby preserving privacy.",7/17/2023,"FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.","ISIC 2019 Challenge Dataset: Primarily contains images of white skin 21,000 dermoscopic images across five diagnostic categories:        Basal Cell Carcinoma (BCC)        Dermatofibroma (DF)        Melanoma (MEL)        Melanocytic Nevus (NV)        Squamous Cell Carcinoma (SCC);  AtlasDerm: Focuses on brown skin tones. Subset Used: 618 images across the same five diagnostic categories. Dermnet: Contains images spanning 23 types of dermatological diseases.    Subset Used: 276 images within the five targeted diagnostic categories. DarkDerm: Established by the authors of this study to represent dark skin tones. Subset Used: 216 images across the five diagnostic categories.","Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, Shi Y, Hu J. Federated self-supervised contrastive learning and masked autoencoder for dermatological disease diagnosis. arXiv preprint arXiv:2208.11278. 2022 Aug 24. doi: 10.48550/arxiv.2208.11278",Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis,Medical Records and Diagnostic Processes,Clinical Practice,2022,NOT LLM,49
86,English,"Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)",Squamous Cell Carcinoma,64.80%,22000,Custom Ai,"FedMAE (ViT-based), Local Fine-Tuning //FedMAE is based on Masked Autoencoders (MAE) and utilizes Vision Transformers (ViT) as the backbone architecture",7/17/2023,"FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.","ISIC 2019 Challenge Dataset: Primarily contains images of white skin 21,000 dermoscopic images across five diagnostic categories:        Basal Cell Carcinoma (BCC)        Dermatofibroma (DF)        Melanoma (MEL)        Melanocytic Nevus (NV)        Squamous Cell Carcinoma (SCC);  AtlasDerm: Focuses on brown skin tones. Subset Used: 618 images across the same five diagnostic categories. Dermnet: Contains images spanning 23 types of dermatological diseases.    Subset Used: 276 images within the five targeted diagnostic categories. DarkDerm: Established by the authors of this study to represent dark skin tones. Subset Used: 216 images across the five diagnostic categories.","Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, Shi Y, Hu J. Federated self-supervised contrastive learning and masked autoencoder for dermatological disease diagnosis. arXiv preprint arXiv:2208.11278. 2022 Aug 24. doi: 10.48550/arxiv.2208.11278",Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis,Medical Records and Diagnostic Processes,Clinical Practice,2022,"NOT LLM, VISION TRANSFORMERS",49
87,English,"Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)",Squamous Cell Carcinoma,70.72%,22000,Custom Ai,"FedCLF (CNN-based), Federated Fine-Tuning // Federated Contrastive Learning with Feature Sharing (FedCLF) is built upon Contrastive Learning (CL). It enables effective learning from decentralized unlabeled data by sharing features rather than raw data, thereby preserving privacy.",7/17/2023,"FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.","ISIC 2019 Challenge Dataset: Primarily contains images of white skin 21,000 dermoscopic images across five diagnostic categories:        Basal Cell Carcinoma (BCC)        Dermatofibroma (DF)        Melanoma (MEL)        Melanocytic Nevus (NV)        Squamous Cell Carcinoma (SCC);  AtlasDerm: Focuses on brown skin tones. Subset Used: 618 images across the same five diagnostic categories. Dermnet: Contains images spanning 23 types of dermatological diseases.    Subset Used: 276 images within the five targeted diagnostic categories. DarkDerm: Established by the authors of this study to represent dark skin tones. Subset Used: 216 images across the five diagnostic categories.","Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, Shi Y, Hu J. Federated self-supervised contrastive learning and masked autoencoder for dermatological disease diagnosis. arXiv preprint arXiv:2208.11278. 2022 Aug 24. doi: 10.48550/arxiv.2208.11278",Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis,Medical Records and Diagnostic Processes,Clinical Practice,2022,NOT LLM,49
88,English,"Image-based diagnostics: dermoscopic images across the five diagnostic categories: Basal Cell Carcinoma (BCC), Dermatofibroma (DF), Melanoma (MEL), Melanocytic Nevus (NV), Squamous Cell Carcinoma (SCC)",Squamous Cell Carcinoma,74.77%,22000,Custom Ai,"FedMAE (ViT-based), Federated Fine-Tuning//FedMAE is based on Masked Autoencoders (MAE) and utilizes Vision Transformers (ViT) as the backbone architecture",7/17/2023,"FedMAE exhibits the highest accuracy in federated fine-tuning, demonstrating its potential for effective dermatological disease diagnosis using federated self-supervised learning. FedMAE consistently outperforms other methods in both local and federated fine-tuning scenarios, highlighting the effectiveness of Vision Transformers combined with Masked Autoencoders in federated learning settings.","ISIC 2019 Challenge Dataset: Primarily contains images of white skin 21,000 dermoscopic images across five diagnostic categories:        Basal Cell Carcinoma (BCC)        Dermatofibroma (DF)        Melanoma (MEL)        Melanocytic Nevus (NV)        Squamous Cell Carcinoma (SCC);  AtlasDerm: Focuses on brown skin tones. Subset Used: 618 images across the same five diagnostic categories. Dermnet: Contains images spanning 23 types of dermatological diseases.    Subset Used: 276 images within the five targeted diagnostic categories. DarkDerm: Established by the authors of this study to represent dark skin tones. Subset Used: 216 images across the five diagnostic categories.","Wu Y, Zeng D, Wang Z, Sheng Y, Yang L, James AJ, Shi Y, Hu J. Federated self-supervised contrastive learning and masked autoencoder for dermatological disease diagnosis. arXiv preprint arXiv:2208.11278. 2022 Aug 24. doi: 10.48550/arxiv.2208.11278",Federated Self-Supervised Contrastive Learning and Masked Autoencoder for Dermatological Disease Diagnosis,Medical Records and Diagnostic Processes,Clinical Practice,2022,"NOT LLM, VISION TRANSFORMERS",49
89,English,NBME-Free-Step1,"Skin anatomy, lesions, dermatitis, blistering diseases, pigment disorders, fungal infections, nevi, nails, Basic science + foundational knowledge",56.00%,87,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"On the four datasets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBMEFree-Step2, ChatGPT achieved accuracies of 44%, 42%, 64.4%, and 57.8%. The model demonstrated a significant decrease in performance as question difficulty increased (P=.012) within the AMBOSSStep1 dataset. By performing at greater than 60% threshold on the NBME-FreeStep-1 dataset we show that the model is comparable to a third year medical student. ","Two novel sets of multiple choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the userbase. The second, was the National Board of Medical Examiners (NBME) Free 120-question exams. ","Aidan Gilson, Conrad Safranek, Thomas Huang, Vimig Socrates, Ling Chi, R. Andrew Taylor, David Chartash ""How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment"" medRxiv 2022.12.23.22283901; doi: https://doi.org/10.1101/2022.12.23.22283901",How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment,United States Medical Licensing Examination,Professional Education,2022,ChatGPT-3.5,0
90,English,NBME-Free-Step2,"Rashes/infections, skin cancers, dermatologic emergencies, diagnosis & management, Clinical application and patient care",59.00%,102,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"On the four datasets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBMEFree-Step2, ChatGPT achieved accuracies of 44%, 42%, 64.4%, and 57.8%. The model demonstrated a significant decrease in performance as question difficulty increased (P=.012) within the AMBOSSStep1 dataset. By performing at greater than 60% threshold on the NBME-FreeStep-1 dataset we show that the model is comparable to a third year medical student. ","Two novel sets of multiple choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the userbase. The second, was the National Board of Medical Examiners (NBME) Free 120-question exams. ","Aidan Gilson, Conrad Safranek, Thomas Huang, Vimig Socrates, Ling Chi, R. Andrew Taylor, David Chartash ""How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment"" medRxiv 2022.12.23.22283901; doi: https://doi.org/10.1101/2022.12.23.22283901",How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment,United States Medical Licensing Examination,Professional Education,2022,ChatGPT-3.5,0
91,English,AMBOSS-Step1,"Similar to NBME Step?1—rash types, inflammatory diseases, histopathology, pathology of skin, High-yield foundational derm",44.00%,100,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"On the four datasets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBMEFree-Step2, ChatGPT achieved accuracies of 44%, 42%, 64.4%, and 57.8%. The model demonstrated a significant decrease in performance as question difficulty increased (P=.012) within the AMBOSSStep1 dataset. By performing at greater than 60% threshold on the NBME-FreeStep-1 dataset we show that the model is comparable to a third year medical student. ","Two novel sets of multiple choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the userbase. The second, was the National Board of Medical Examiners (NBME) Free 120-question exams. ","Aidan Gilson, Conrad Safranek, Thomas Huang, Vimig Socrates, Ling Chi, R. Andrew Taylor, David Chartash ""How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment"" medRxiv 2022.12.23.22283901; doi: https://doi.org/10.1101/2022.12.23.22283901",How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment,United States Medical Licensing Examination,Professional Education,2022,ChatGPT-3.5,0
92,English,AMBOSS-Step2,"Management of derm conditions, systemic associations, guidelines, special populations, Clinical scenarios & treatment approach",42.00%,100,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"On the four datasets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBMEFree-Step2, ChatGPT achieved accuracies of 44%, 42%, 64.4%, and 57.8%. The model demonstrated a significant decrease in performance as question difficulty increased (P=.012) within the AMBOSSStep1 dataset. By performing at greater than 60% threshold on the NBME-FreeStep-1 dataset we show that the model is comparable to a third year medical student. ","Two novel sets of multiple choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the userbase. The second, was the National Board of Medical Examiners (NBME) Free 120-question exams. ","Aidan Gilson, Conrad Safranek, Thomas Huang, Vimig Socrates, Ling Chi, R. Andrew Taylor, David Chartash ""How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment"" medRxiv 2022.12.23.22283901; doi: https://doi.org/10.1101/2022.12.23.22283901",How Does ChatGPT Perform on the Medical Licensing Exams? The Implications of Large Language Models for Medical Education and Knowledge Assessment,United States Medical Licensing Examination,Professional Education,2022,ChatGPT-3.5,0
93,English,Image-based diagnostics,"Alopecia, Alopecia areata",Better than other forms of alopecia,N/A,ChatGPT 4V,ChatGPT-4V,11/1/2023,"ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.",Not discussed,"Willow D. Pastard, Willow Pastard, Zane Sejdiu, Alexis Arza, James Cross, Razmig Garabet, Anna Chacon, Ellen N. Pritchett, A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones, Journal of the National Medical Association, Volume 116, Issue 4, 2024, Page 426, ISSN 0027-9684, https://doi.org/10.1016/j.jnma.2024.07.035. (https://www.sciencedirect.com/science/article/pii/S0027968424001160)",A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,27
94,English,Image-based diagnostics,"Alopecia, Androgenetic alopecia",Better than traction alopecia and central centrifugal cicatrical alopecia,N/A,ChatGPT 4V,ChatGPT-4V,11/1/2023,"ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.",Not discussed,"Willow D. Pastard, Willow Pastard, Zane Sejdiu, Alexis Arza, James Cross, Razmig Garabet, Anna Chacon, Ellen N. Pritchett, A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones, Journal of the National Medical Association, Volume 116, Issue 4, 2024, Page 426, ISSN 0027-9684, https://doi.org/10.1016/j.jnma.2024.07.035. (https://www.sciencedirect.com/science/article/pii/S0027968424001160)",A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,27
95,English,Image-based diagnostics,"Alopecia, Central centrifugal cicatricial alopecia in dark skin tones",Worse than most other forms of alopecia,N/A,ChatGPT 4V,ChatGPT-4V,11/1/2023,"ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.",Not discussed,"Willow D. Pastard, Willow Pastard, Zane Sejdiu, Alexis Arza, James Cross, Razmig Garabet, Anna Chacon, Ellen N. Pritchett, A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones, Journal of the National Medical Association, Volume 116, Issue 4, 2024, Page 426, ISSN 0027-9684, https://doi.org/10.1016/j.jnma.2024.07.035. (https://www.sciencedirect.com/science/article/pii/S0027968424001160)",A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,27
96,English,Image-based diagnostics,"Alopecia, Traction alopecia in dark skin tones; High Rate of misclassification",Worse than other forms of alopecia,N/A,ChatGPT 4V,ChatGPT-4V,11/1/2023,"ChatGPT was more likely to correctly identify disease in lighter skin, notably for alopecia areata (p<.001) and androgenetic alopecia (p=.003). This trend was also seen in overall diagnosis rates (p<.001). Interestingly, the program repeatedly incorrectly identified 24.48% of all hair conditions in dark skin as traction alopecia. Additionally, while initially this study sought to explore ChatGPT's ability to diagnose common nail disorders across skin tones this could not be completed due to the insufficient availability of images depicting nail disorders in darker skin.",Not discussed,"Willow D. Pastard, Willow Pastard, Zane Sejdiu, Alexis Arza, James Cross, Razmig Garabet, Anna Chacon, Ellen N. Pritchett, A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones, Journal of the National Medical Association, Volume 116, Issue 4, 2024, Page 426, ISSN 0027-9684, https://doi.org/10.1016/j.jnma.2024.07.035. (https://www.sciencedirect.com/science/article/pii/S0027968424001160)",A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,27
97,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
98,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
99,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
100,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
101,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
102,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
103,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
104,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
105,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
106,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
107,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
108,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
109,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
110,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
111,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
112,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
113,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
114,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
115,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
116,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
117,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
118,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
119,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
120,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
121,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
122,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
123,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
124,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
125,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
126,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
127,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
128,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
129,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
130,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
131,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
132,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
133,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
134,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
135,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
136,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
137,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
138,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
139,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
140,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
141,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
142,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
143,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
144,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
145,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
146,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
147,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
148,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
149,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
150,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
151,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
152,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
153,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
154,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
155,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
156,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
157,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
158,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
159,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
160,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
161,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
162,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
163,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
164,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
165,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
166,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
167,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
168,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
169,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
170,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
171,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
172,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
173,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
174,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
175,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
176,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
177,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
178,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
179,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
180,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
181,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
182,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
183,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
184,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
185,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
186,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
187,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
188,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
189,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
190,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
191,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
192,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
193,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
194,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
195,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
196,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
197,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
198,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
199,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
200,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
201,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
202,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
203,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
204,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
205,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
206,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
207,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
208,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
209,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
210,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
211,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
212,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
213,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
214,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
215,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
216,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
217,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
218,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
219,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
220,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
221,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
222,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
223,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
224,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
225,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
226,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
227,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
228,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
229,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
230,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
231,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
232,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
233,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
234,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
235,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
236,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
237,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
238,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
239,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
240,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
241,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
242,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
243,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
244,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
245,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
246,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
247,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
248,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
249,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
250,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
251,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
252,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
253,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
254,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
255,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
256,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
257,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
258,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
259,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
260,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
261,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
262,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
263,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
264,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
265,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
266,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
267,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
268,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
269,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
270,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
271,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
272,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
273,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Atopic eczema,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
274,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Psoriasis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
275,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Abscess,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
276,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Discoid lupus erythematosus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
277,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Clavus,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
278,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Warts,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
279,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Basal Cell Carcinoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
280,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Melanoma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
281,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Actinic Keratosis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
282,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Chronic urticaria,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
283,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pyoderma gangrenosum,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
284,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Shingles,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
285,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Fungal nail infections,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
286,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pityriasis versicolor,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
287,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Tinea corporis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
288,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Impetigo,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
289,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Erythrasma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
290,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Dermatomyositis,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
291,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Scleroderma,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
292,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Morphea,20.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
293,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Atopic eczema,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
294,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Psoriasis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
295,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Abscess,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
296,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Discoid lupus erythematosus,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
297,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Warts,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
298,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Basal Cell Carcinoma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
299,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Melanoma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
300,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Actinic Keratosis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
301,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Fungal nail infections,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
302,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pityriasis versicolor,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
303,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Tinea corporis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
304,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Erythrasma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
305,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Morphea,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
306,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Impetigo,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
307,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Erythrasma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
308,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Morphea,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
309,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Atopic eczema,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
310,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Psoriasis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
311,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Clavus,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
312,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Melanoma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
313,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Chronic urticaria,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
314,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
315,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Fungal nail infections,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
316,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pityriasis versicolor,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
317,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Tinea corporis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
318,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Impetigo,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
319,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Erythrasma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
320,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Dermatomyositis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
321,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Scleroderma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
322,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Morphea,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
323,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Atopic eczema,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
324,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Abscess,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
325,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Clavus,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
326,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Basal Cell Carcinoma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
327,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Melanoma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
328,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pyoderma gangrenosum,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
329,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Fungal nail infections,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
330,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pityriasis versicolor,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
331,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Tinea corporis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
332,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Impetigo,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
333,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Erythrasma,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
334,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Dermatomyositis,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
335,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Morphea,20.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
336,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
337,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
338,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
339,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
340,English,information presented is balanced and unbiased,Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
341,English,information presented is balanced and unbiased,Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
342,English,information presented is balanced and unbiased,Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
343,English,information presented is balanced and unbiased,Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
344,English,information presented is balanced and unbiased,Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
345,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Atopic eczema,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
346,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Abscess,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
347,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
348,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Melanoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
349,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Chronic urticaria,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
350,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Shingles,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
351,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
352,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pityriasis versicolor,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
353,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
354,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
355,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Dermatomyositis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
356,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Atopic eczema,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
357,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Abscess,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
358,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
359,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
360,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Melanoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
361,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Chronic urticaria,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
362,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
363,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
364,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Tinea corporis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
365,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
366,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
367,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Dermatomyositis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
368,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Abscess,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
369,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
370,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
371,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Melanoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
372,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Chronic urticaria,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
373,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
374,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Shingles,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
375,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
376,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pityriasis versicolor,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
377,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Tinea corporis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
378,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
379,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
380,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Dermatomyositis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
381,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Scleroderma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
382,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Morphea,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
383,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Atopic eczema,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
384,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Abscess,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
385,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Discoid lupus erythematosus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
386,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
387,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
388,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Melanoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
389,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Actinic Keratosis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
390,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Chronic urticaria,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
391,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
392,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Shingles,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
393,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
394,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pityriasis versicolor,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
395,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Tinea corporis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
396,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
397,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
398,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Dermatomyositis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
399,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Scleroderma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
400,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Morphea,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
401,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Abscess,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
402,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Discoid lupus erythematosus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
403,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
404,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Basal Cell Carcinoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
405,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Melanoma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
406,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Actinic Keratosis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
407,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Chronic urticaria,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
408,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
409,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
410,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pityriasis versicolor,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
411,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Tinea corporis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
412,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
413,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
414,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Dermatomyositis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
415,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Morphea,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
416,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
417,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
418,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
419,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
420,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Atopic eczema,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
421,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
422,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
423,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pityriasis versicolor,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
424,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Tinea corporis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
425,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
426,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
427,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Clavus,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
428,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pyoderma gangrenosum,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
429,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Fungal nail infections,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
430,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Tinea corporis,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
431,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Impetigo,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
432,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Erythrasma,20.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
433,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Melanoma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
434,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Fungal nail infections,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
435,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pityriasis versicolor,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
436,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Tinea corporis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
437,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Impetigo,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
438,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Erythrasma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
439,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Clavus,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
440,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
441,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Fungal nail infections,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
442,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pityriasis versicolor,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
443,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Dermatomyositis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
444,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Morphea,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
445,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Abscess,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
446,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Warts,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
447,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Melanoma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
448,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Chronic urticaria,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
449,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
450,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Shingles,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
451,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Fungal nail infections,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
452,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pityriasis versicolor,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
453,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Tinea corporis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
454,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Impetigo,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
455,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Erythrasma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
456,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Dermatomyositis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
457,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Morphea,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
458,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Atopic eczema,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
459,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Abscess,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
460,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Warts,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
461,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Basal Cell Carcinoma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
462,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Melanoma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
463,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Actinic Keratosis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
464,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Chronic urticaria,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
465,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pyoderma gangrenosum,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
466,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Shingles,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
467,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Fungal nail infections,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
468,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Pityriasis versicolor,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
469,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Tinea corporis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
470,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Impetigo,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
471,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Erythrasma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
472,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Dermatomyositis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
473,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Scleroderma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
474,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Morphea,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
475,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Psoriasis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
476,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Abscess,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
477,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Discoid lupus erythematosus,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
478,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Clavus,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
479,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Warts,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
480,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Basal Cell Carcinoma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
481,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Melanoma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
482,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Actinic Keratosis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
483,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Chronic urticaria,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
484,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pyoderma gangrenosum,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
485,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Shingles,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
486,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Fungal nail infections,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
487,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Pityriasis versicolor,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
488,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Tinea corporis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
489,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Impetigo,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
490,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Erythrasma,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
491,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Dermatomyositis,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
492,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Morphea,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
493,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pityriasis versicolor,20.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
494,English,"Patient Education, Dermatology medical content generated, falsness",Patient Education,35.00%,60,Claude 1,Claude-instant-v1.0,8/9/2023,"Claude-instant-v1.0 demonstrated the highest falseness ratings in ophthalmology (68.4%, 95% CI 47%-89.9%) and dermatology (65%, 95% CI 43.6%-86.4%), while GPT-3.5-Turbo exhibited the lowest rating in dermatology (0%, 95% CI 0%-0%). ","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists.","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
495,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Atopic eczema,40.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
496,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Abscess,40.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
497,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Basal Cell Carcinoma,40.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
498,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pyoderma gangrenosum,40.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
499,English,information presented is balanced and unbiased,Clavus,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
500,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Clavus,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
501,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Chronic urticaria,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
502,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pyoderma gangrenosum,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
503,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Shingles,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
504,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Dermatomyositis,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
505,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Scleroderma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
506,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Atopic eczema,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
507,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pyoderma gangrenosum,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
508,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Shingles,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
509,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Fungal nail infections,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
510,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Erythrasma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
511,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Scleroderma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
512,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Psoriasis,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
513,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Basal Cell Carcinoma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
514,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Melanoma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
515,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pyoderma gangrenosum,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
516,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Shingles,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
517,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Fungal nail infections,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
518,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Tinea corporis,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
519,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Dermatomyositis,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
520,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Scleroderma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
521,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Abscess,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
522,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Discoid lupus erythematosus,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
523,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Basal Cell Carcinoma,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
524,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Shingles,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
525,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Psoriasis,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
526,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Discoid lupus erythematosus,40.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
527,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Fungal nail infections,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
528,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Chronic urticaria,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
529,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Pityriasis versicolor,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
530,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Tinea corporis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
531,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Dermatomyositis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
532,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Psoriasis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
533,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Discoid lupus erythematosus,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
534,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Basal Cell Carcinoma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
535,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Actinic Keratosis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
536,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pyoderma gangrenosum,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
537,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Tinea corporis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
538,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Scleroderma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
539,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Morphea,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
540,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Psoriasis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
541,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Discoid lupus erythematosus,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
542,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Shingles,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
543,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pityriasis versicolor,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
544,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Scleroderma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
545,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Psoriasis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
546,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Discoid lupus erythematosus,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
547,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Actinic Keratosis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
548,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Psoriasis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
549,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Warts,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
550,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Psoriasis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
551,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Warts,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
552,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Shingles,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
553,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Scleroderma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
554,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Abscess,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
555,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Clavus,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
556,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Melanoma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
557,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Dermatomyositis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
558,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Scleroderma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
559,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Melanoma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
560,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Chronic urticaria,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
561,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pityriasis versicolor,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
562,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Dermatomyositis,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
563,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Scleroderma,40.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
564,English,information presented is balanced and unbiased,Fungal nail infections,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
565,English,information presented is balanced and unbiased,Pityriasis versicolor,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
566,English,information presented is balanced and unbiased,Tinea corporis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
567,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Atopic eczema,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
568,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Psoriasis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
569,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Abscess,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
570,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Discoid lupus erythematosus,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
571,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Clavus,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
572,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Basal Cell Carcinoma,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
573,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Actinic Keratosis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
574,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Chronic urticaria,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
575,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Pyoderma gangrenosum,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
576,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Shingles,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
577,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Scleroderma,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
578,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Morphea,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
579,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Psoriasis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
580,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Abscess,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
581,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Warts,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
582,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Melanoma,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
583,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Chronic urticaria,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
584,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Shingles,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
585,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Tinea corporis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
586,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Impetigo,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
587,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Erythrasma,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
588,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Psoriasis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
589,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Clavus,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
590,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Scleroderma,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
591,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Psoriasis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
592,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Discoid lupus erythematosus,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
593,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Clavus,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
594,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Atopic eczema,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
595,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Scleroderma,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
596,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Morphea,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
597,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Chronic urticaria,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
598,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Fungal nail infections,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
599,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pityriasis versicolor,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
600,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Tinea corporis,40.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
601,English,"atopic eczema, psoriasis, an abscess, discoid lupus erythematosus, a clavus, warts, basal cell carcinoma, malignant melanoma, actinic keratosis, chronic urticaria, pyoderma gangrenosum, shingles, fungal nail infections, pityriasis versicolor, tinea corporis, impetigo, erythrasma, dermatomyositis, scleroderma, morphea)","pediatric, infectious and autoimmune dermatology",48.30%,180,Claude 1,Claude-instant-v1.0,8/9/2023,"The overall accuracy, defined as the absence of harmfulness and falseness, was highest for GPT-3.5-Turbo (88.3%, 95% CI 80.1%-96.5%) and was lowest for Claude-instant-v1.0 (48.3%, 95% CI 35.6%-61.1%).","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists.","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
602,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Clavus,60.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
603,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Warts,60.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
604,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Clavus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
605,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Tinea corporis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
606,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Impetigo,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
607,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Psoriasis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
608,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Melanoma,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
609,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Pityriasis versicolor,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
610,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Tinea corporis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
611,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Impetigo,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
612,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Atopic eczema,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
613,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Clavus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
614,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Chronic urticaria,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
615,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Pityriasis versicolor,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
616,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Warts,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
617,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Chronic urticaria,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
618,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Shingles,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
619,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Scleroderma,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
620,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Abscess,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
621,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Discoid lupus erythematosus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
622,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Clavus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
623,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Basal Cell Carcinoma,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
624,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Shingles,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
625,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Fungal nail infections,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
626,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pityriasis versicolor,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
627,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Tinea corporis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
628,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Discoid lupus erythematosus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
629,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Clavus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
630,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pityriasis versicolor,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
631,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Tinea corporis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
632,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Atopic eczema,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
633,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Discoid lupus erythematosus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
634,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Clavus,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
635,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Warts,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
636,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Basal Cell Carcinoma,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
637,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Actinic Keratosis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
638,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Pityriasis versicolor,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
639,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Tinea corporis,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
640,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Erythrasma,60.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
641,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Abscess,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
642,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Chronic urticaria,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
643,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Shingles,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
644,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pityriasis versicolor,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
645,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Tinea corporis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
646,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Dermatomyositis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
647,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Scleroderma,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
648,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Abscess,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
649,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Discoid lupus erythematosus,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
650,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Melanoma,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
651,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Scleroderma,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
652,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Warts,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
653,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Actinic Keratosis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
654,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Basal Cell Carcinoma,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
655,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Actinic Keratosis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
656,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Chronic urticaria,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
657,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Shingles,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
658,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Abscess,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
659,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Discoid lupus erythematosus,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
660,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Basal Cell Carcinoma,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
661,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Actinic Keratosis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
662,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Shingles,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
663,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Clavus,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
664,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Tinea corporis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
665,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Dermatomyositis,60.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
666,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Chronic urticaria,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
667,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pyoderma gangrenosum,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
668,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Fungal nail infections,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
669,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pityriasis versicolor,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
670,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Tinea corporis,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
671,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Abscess,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
672,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Chronic urticaria,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
673,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Pyoderma gangrenosum,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
674,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Shingles,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
675,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Warts,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
676,English,"Attribution evaluation, Citation evaluation, Source transparency, Evidence support evaluation: Additional Sources Listed",Dermatomyositis,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
677,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Atopic eczema,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
678,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Discoid lupus erythematosus,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
679,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Actinic Keratosis,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
680,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Fungal nail infections,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
681,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Chronic urticaria,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
682,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pyoderma gangrenosum,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
683,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Fungal nail infections,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
684,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Tinea corporis,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
685,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Impetigo,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
686,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Erythrasma,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
687,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Dermatomyositis,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
688,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Scleroderma,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
689,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Clavus,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
690,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pyoderma gangrenosum,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
691,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Shingles,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
692,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Erythrasma,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
693,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Scleroderma,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
694,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Morphea,60.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
695,English,harmfulness,Abscess,80.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
696,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Shingles,80.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
697,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Shingles,80.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
698,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Atopic eczema,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
699,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Psoriasis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
700,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Abscess,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
701,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Discoid lupus erythematosus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
702,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
703,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Basal Cell Carcinoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
704,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Melanoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
705,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
706,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pityriasis versicolor,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
707,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Tinea corporis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
708,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Impetigo,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
709,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Erythrasma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
710,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Scleroderma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
711,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Atopic eczema,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
712,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Psoriasis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
713,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Discoid lupus erythematosus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
714,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
715,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Basal Cell Carcinoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
716,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Melanoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
717,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Chronic urticaria,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
718,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Impetigo,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
719,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Erythrasma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
720,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Scleroderma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
721,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Morphea,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
722,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Abscess,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
723,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Discoid lupus erythematosus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
724,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Clavus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
725,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
726,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Basal Cell Carcinoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
727,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
728,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Chronic urticaria,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
729,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Dermatomyositis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
730,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Morphea,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
731,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Abscess,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
732,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Discoid lupus erythematosus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
733,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
734,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
735,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
736,English,"Risk communication, Adverse effects description: risks of each treatment procedure described",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
737,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
738,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Atopic eczema,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
739,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Psoriasis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
740,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
741,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Melanoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
742,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
743,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Chronic urticaria,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
744,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Pyoderma gangrenosum,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
745,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Impetigo,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
746,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Erythrasma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
747,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Dermatomyositis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
748,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Scleroderma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
749,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Morphea,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
750,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Atopic eczema,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
751,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Psoriasis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
752,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Abscess,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
753,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Warts,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
754,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Basal Cell Carcinoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
755,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Melanoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
756,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Actinic Keratosis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
757,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Chronic urticaria,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
758,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Pyoderma gangrenosum,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
759,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Shingles,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
760,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Fungal nail infections,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
761,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Impetigo,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
762,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Erythrasma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
763,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Scleroderma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
764,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Morphea,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
765,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Discoid lupus erythematosus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
766,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Clavus,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
767,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Basal Cell Carcinoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
768,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Tinea corporis,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
769,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Erythrasma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
770,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Melanoma,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
771,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Shingles,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
772,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Fungal nail infections,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
773,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Impetigo,80.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
774,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Atopic eczema,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
775,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Psoriasis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
776,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Discoid lupus erythematosus,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
777,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Warts,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
778,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Basal Cell Carcinoma,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
779,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Melanoma,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
780,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Actinic Keratosis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
781,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Atopic eczema,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
782,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Psoriasis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
783,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Warts,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
784,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Actinic Keratosis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
785,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Shingles,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
786,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Morphea,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
787,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Atopic eczema,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
788,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Warts,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
789,English,"Quality of life impact, QoL assessment, Patient-centered outcomes, Well-being impact evaluation: Is it described how the treatment procedures affect quality of life",Atopic eczema,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
790,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Abscess,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
791,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Clavus,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
792,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Melanoma,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
793,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Chronic urticaria,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
794,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Shingles,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
795,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pityriasis versicolor,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
796,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Tinea corporis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
797,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Dermatomyositis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
798,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Scleroderma,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
799,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Psoriasis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
800,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Discoid lupus erythematosus,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
801,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Warts,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
802,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Morphea,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
803,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Atopic eczema,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
804,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Psoriasis,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
805,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Warts,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
806,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Morphea,80.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
807,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Atopic eczema,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
808,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Psoriasis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
809,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Abscess,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
810,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Warts,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
811,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Basal Cell Carcinoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
812,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Melanoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
813,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Actinic Keratosis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
814,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Shingles,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
815,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Impetigo,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
816,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Erythrasma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
817,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Dermatomyositis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
818,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Scleroderma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
819,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Morphea,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
820,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Atopic eczema,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
821,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Psoriasis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
822,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Clavus,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
823,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Warts,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
824,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Basal Cell Carcinoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
825,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Melanoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
826,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Actinic Keratosis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
827,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Impetigo,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
828,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Erythrasma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
829,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Dermatomyositis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
830,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Scleroderma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
831,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Morphea,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
832,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Basal Cell Carcinoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
833,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Actinic Keratosis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
834,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Scleroderma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
835,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Atopic eczema,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
836,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Discoid lupus erythematosus,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
837,English,"Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: Outcome communication, Positive impact description, Benefit explanation, Treatment benefit assessment: benefits of each treatment procedure described",Basal Cell Carcinoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
838,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Abscess,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
839,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Chronic urticaria,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
840,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pyoderma gangrenosum,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
841,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Shingles,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
842,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Tinea corporis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
843,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Erythrasma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
844,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Scleroderma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
845,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Atopic eczema,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
846,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Psoriasis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
847,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Abscess,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
848,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Clavus,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
849,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Warts,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
850,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Basal Cell Carcinoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
851,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Melanoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
852,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Shingles,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
853,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Atopic eczema,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
854,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Psoriasis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
855,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Abscess,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
856,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Discoid lupus erythematosus,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
857,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Warts,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
858,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Basal Cell Carcinoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
859,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Melanoma,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
860,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Actinic Keratosis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
861,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Impetigo,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
862,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Dermatomyositis,80.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
863,English,Rubric-based evaluation of AI model performance,"pediatric, infectious and autoimmune dermatology",88.30%,180,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"The overall accuracy, defined as the absence of harmfulness and falseness, was highest for GPT-3.5-Turbo (88.3%, 95% CI 80.1%-96.5%) and was lowest for Claude-instant-v1.0 (48.3%, 95% CI 35.6%-61.1%).","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists. 20 dermatologic diseases included atopic eczema, psoriasis, an abscess, discoid lupus erythematosus, a clavus, warts, basal cell carcinoma, malignant melanoma, actinic keratosis, chronic urticaria, pyoderma gangrenosum, shingles, fungal nail infections, pityriasis versicolor, tinea corporis, impetigo, erythrasma, dermatomyositis, scleroderma, morphea)","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
864,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Atopic eczema,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists. 20 dermatologic diseases included atopic eczema, psoriasis, an abscess, discoid lupus erythematosus, a clavus, warts, basal cell carcinoma, malignant melanoma, actinic keratosis, chronic urticaria, pyoderma gangrenosum, shingles, fungal nail infections, pityriasis versicolor, tinea corporis, impetigo, erythrasma, dermatomyositis, scleroderma, morphea)","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
865,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Psoriasis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists. 20 dermatologic diseases included atopic eczema, psoriasis, an abscess, discoid lupus erythematosus, a clavus, warts, basal cell carcinoma, malignant melanoma, actinic keratosis, chronic urticaria, pyoderma gangrenosum, shingles, fungal nail infections, pityriasis versicolor, tinea corporis, impetigo, erythrasma, dermatomyositis, scleroderma, morphea)","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
866,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Discoid lupus erythematosus,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists. 20 dermatologic diseases included atopic eczema, psoriasis, an abscess, discoid lupus erythematosus, a clavus, warts, basal cell carcinoma, malignant melanoma, actinic keratosis, chronic urticaria, pyoderma gangrenosum, shingles, fungal nail infections, pityriasis versicolor, tinea corporis, impetigo, erythrasma, dermatomyositis, scleroderma, morphea)","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
867,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Clavus,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
868,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Warts,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
869,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Basal Cell Carcinoma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
870,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Melanoma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
871,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Actinic Keratosis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
872,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Chronic urticaria,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
873,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pyoderma gangrenosum,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
874,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Fungal nail infections,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
875,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pityriasis versicolor,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
876,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Tinea corporis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
877,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Impetigo,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
878,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Erythrasma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
879,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Dermatomyositis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
880,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Scleroderma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
881,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Morphea,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
882,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Atopic eczema,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
883,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Psoriasis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
884,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Abscess,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
885,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: evaluation, Safety assessment: NOT Harmfulness",Discoid lupus erythematosus,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
886,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: evaluation, Safety assessment: NOT Harmfulness",Basal Cell Carcinoma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
887,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Melanoma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
888,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Actinic Keratosis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
889,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Chronic urticaria,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
890,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pyoderma gangrenosum,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
891,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Fungal nail infections,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
892,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pityriasis versicolor,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
893,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Tinea corporis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
894,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Impetigo,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
895,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Erythrasma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
896,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Dermatomyositis,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
897,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Scleroderma,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
898,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Morphea,100.00%,3,Bloomz,bloomz,12/26/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,BLOOM series,36
899,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Chronic urticaria,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
900,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Pyoderma gangrenosum,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
901,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Shingles,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
902,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Fungal nail infections,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
903,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Dermatomyositis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
904,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Morphea,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
905,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Abscess,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
906,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Actinic Keratosis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
907,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Pyoderma gangrenosum,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
908,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Shingles,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
909,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Fungal nail infections,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
910,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Pityriasis versicolor,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
911,English,"Bias evaluation, Fairness evaluation, Objectivity evaluation, Impartiality assessment: information presented is balanced and unbiased",Dermatomyositis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
912,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Atopic eczema,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
913,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Psoriasis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
914,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Abscess,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
915,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Discoid lupus erythematosus,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
916,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Clavus,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
917,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Warts,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
918,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Basal Cell Carcinoma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
919,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Melanoma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
920,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Actinic Keratosis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
921,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Chronic urticaria,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
922,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pyoderma gangrenosum,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
923,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Shingles,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
924,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Fungal nail infections,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
925,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pityriasis versicolor,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
926,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Tinea corporis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
927,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Impetigo,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
928,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Erythrasma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
929,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Dermatomyositis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
930,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Scleroderma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
931,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Morphea,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
932,English,"Information quality assessment, Content quality evaluation, Text reliability evaluation, Informational adequacy: overall quality of generated text as a source of information",Dermatomyositis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
933,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Atopic eczema,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
934,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Psoriasis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
935,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Abscess,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
936,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Warts,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
937,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Melanoma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
938,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Actinic Keratosis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
939,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Chronic urticaria,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
940,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pyoderma gangrenosum,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
941,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Shingles,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
942,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Fungal nail infections,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
943,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pityriasis versicolor,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
944,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Impetigo,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
945,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Dermatomyositis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
946,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Scleroderma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
947,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Morphea,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
948,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Psoriasis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
949,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Abscess,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
950,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Chronic urticaria,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
951,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Pyoderma gangrenosum,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
952,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Dermatomyositis,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
953,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Scleroderma,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
954,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Morphea,100.00%,3,Claude 1,claude-instant-v1.0,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Claude series,36
955,English,"Outcome-based evaluation, Objective-based evaluation",Morphea,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
956,English,information presented is balanced and unbiased,Basal Cell Carcinoma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
957,English,information presented is balanced and unbiased,Morphea,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
958,English,"Clinical reasoning evaluation, Explanatory depth, Treatment rationale assessment: mode of action of each treatment procedure described",Warts,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
959,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Atopic eczema,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
960,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Psoriasis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
961,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Discoid lupus erythematosus,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
962,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Warts,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
963,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Basal Cell Carcinoma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
964,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Actinic Keratosis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
965,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Morphea,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
966,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Atopic eczema,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
967,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Psoriasis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
968,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Abscess,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
969,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Discoid lupus erythematosus,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
970,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Clavus,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
971,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Warts,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
972,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Basal Cell Carcinoma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
973,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Melanoma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
974,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Actinic Keratosis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
975,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Chronic urticaria,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
976,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pyoderma gangrenosum,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
977,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Shingles,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
978,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Fungal nail infections,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
979,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pityriasis versicolor,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
980,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Tinea corporis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
981,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Impetigo,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
982,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Erythrasma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
983,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Dermatomyositis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
984,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Scleroderma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
985,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Morphea,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
986,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Atopic eczema,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
987,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Psoriasis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
988,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Abscess,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
989,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Discoid lupus erythematosus,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
990,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Warts,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
991,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Basal Cell Carcinoma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
992,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Melanoma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
993,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Actinic Keratosis,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
994,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Chronic urticaria,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
995,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Pyoderma gangrenosum,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
996,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Shingles,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
997,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Fungal nail infections,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
998,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Pityriasis versicolor,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
999,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Impetigo,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
1000,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Erythrasma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
1001,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Scleroderma,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
1002,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Morphea,100.00%,3,Command,command-xlarge-nightly,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,Cohere series,36
1003,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Patient Education,100.00%,60,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Claude-instant-v1.0 demonstrated the highest falseness ratings in ophthalmology (68.4%, 95% CI 47%-89.9%) and dermatology (65%, 95% CI 43.6%-86.4%), while GPT-3.5-Turbo exhibited the lowest rating in dermatology (0%, 95% CI 0%-0%). ","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information generated by LLM was ranked by 3 specialist physicians. There were 180 questions total spanning diverse medical disciplines, ChatGPT's thera[y recommendations exhibited an accuracy rate of 57.8% in providing “correct” or “almost correct” responses. These answers were meticulously evaluated by a panel of 17 medical specialists.","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1004,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Discoid lupus erythematosus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1005,English,"Outcome-based evaluation, Objective-based evaluation: Objectives Clear and Achieved",Clavus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1006,English,information presented is balanced and unbiased,Discoid lupus erythematosus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1007,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Atopic eczema,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1008,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Psoriasis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1009,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Discoid lupus erythematosus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1010,English,"a clavus: Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Clavus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1011,English,"warts: Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Warts,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1012,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Basal Cell Carcinoma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1013,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Melanoma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1014,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Actinic Keratosis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1015,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Pityriasis versicolor,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1016,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Impetigo,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1017,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Dermatomyositis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1018,English,"Therapeutic alternatives evaluation, Options presentation, Treatment options disclosure: Is it clearly presented that more than one possible treatment procedure may exist",Morphea,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1019,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Discoid lupus erythematosus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1020,English,"Patient engagement assessment, Shared decision-making support, SDM facilitation, Decision support evaluation: the information is an aid to ""shared decision-making""",Actinic Keratosis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1021,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Atopic eczema,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1022,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Psoriasis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1023,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Abscess,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1024,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Discoid lupus erythematosus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1025,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Clavus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1026,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Warts,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1027,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Basal Cell Carcinoma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1028,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Melanoma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1029,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Actinic Keratosis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1030,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Chronic urticaria,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1031,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pyoderma gangrenosum,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1032,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Shingles,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1033,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Fungal nail infections,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1034,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Pityriasis versicolor,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1035,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Tinea corporis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1036,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Impetigo,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1037,English,"Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: Content safety review, Non-maleficence check, Harm avoidance evaluation, Safety assessment: NOT Harmfulness",Erythrasma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1038,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Dermatomyositis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1039,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Scleroderma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1040,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Morphea,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1041,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Atopic eczema,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1042,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Psoriasis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1043,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Abscess,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1044,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Discoid lupus erythematosus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1045,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Clavus,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1046,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Warts,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1047,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Basal Cell Carcinoma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1048,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Melanoma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1049,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Actinic Keratosis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1050,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Chronic urticaria,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1051,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Pyoderma gangrenosum,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1052,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Shingles,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1053,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Fungal nail infections,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1054,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Pityriasis versicolor,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1055,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Tinea corporis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1056,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Impetigo,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1057,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Erythrasma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1058,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Dermatomyositis,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1059,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Scleroderma,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1060,English,"Misinformation avoidance, Veracity assessment, Truthfulness check, Factual accuracy evaluation: NOT Falsness",Morphea,100.00%,3,ChatGPT 3.5,ChatGPT 3.5 Turbo,8/9/2023,"Score derived from rankings of 3 specialist physicians on a scale 1 - 5, converted to percentages of accuracy/validity","20 dermatologic diseases out of 60 arbitrarily chosen diseases (19 ophthalmologic, 20 dermatologic, and 21 orthopedic)  - dermatology information (therapy recommendation) generated by LLM ranked by 3 specialist physicians","Wilhelm TI, Roos J, Kaczmarczyk R. Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study. J Med Internet Res. 2023 Oct 30;25:e49324. doi: 10.2196/49324. PMID: 37902826; PMCID: PMC10644179.",Large Language Models for Therapy Recommendations Across 3 Clinical Specialties: Comparative Study,Medication Recommendations and Treatment Efficacy,Clinical Practice,2023,OpenAI GPT series,36
1061,English,Readability of Patient Information Leaflets (PILs),"Common skin conditions, Readability",65.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,65% accuracy rated positively for condition-related PILs,10 Patient information leaflets (PILs) generated using ChatGPT,Verran C. Artificial intelligence-generated patient information leaflets: a comparison of contents according to British Association of Dermatologists standards. Clin Exp Dermatol. 2024 Jun 25;49(7):711-714. doi: 10.1093/ced/llad461. PMID: 38169318.,Artificial intelligence-generated patient information leaflets: a comparison of contents according to British Association of Dermatologists standards,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,10
1062,English,"Image-based diagnostics, Clinical images",Common and rare skin conditions,40.00%,15,ChatGPT 4,ChatGPT-4,3/14/2023,"Rating for  relevance, accuracy, and depth (average 2 one the scale of 1 to 5)","5 clinical images were selected from the Danish web atlas, Danderm, depicting various common and rare skin conditions","Nielsen JP, Grønhøj C, Skov L, Gyldenløve M. Usefulness of the large language model ChatGPT (GPT?4) as a diagnostic tool and information source in dermatology. JEADV Clinical Practice. 2024.",Usefulness of the large language model ChatGPT (GPTâ€�4) as a diagnostic tool and information source in dermatology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,19
1063,Japanese,Multiple Choice Questions: Dermatology subject area in the Japanese National Nurse Examinations,Certification,50.00%,6,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,"ChatGPT had a lower percentage of correct answers in some areas, such as pharmacology, social welfare, related law and regulations, endocrinology/metabolism, and dermatology, and a higher percentage of correct answers in the areas of nutrition, pathology, hematology, ophthalmology, otolaryngology, dentistry and dental surgery, and nursing integration and practice. For Basic Knowledge Questions: Average accuracy was 75.1% (SD 3%); for  General Questions: Average accuracy was 64.5% (SD 5%)","240 questions from the Japanese National Nurse Examinations set from 2019 to 2023, including 6 questions in dermatology subject area","Taira K, Itaya T, Hanada A. Performance of the Large Language Model ChatGPT on the National Nurse Examinations in Japan: Evaluation Study. JMIR Nurs. 2023 Jun 27;6:e47305. doi: 10.2196/47305. PMID: 37368470; PMCID: PMC10337249.",Performance of the Large Language Model ChatGPT on the National Nurse Examinations in Japan: Evaluation Study,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,1
1064,English,Diagnosis from clinical history and cutaneous signs recorded by nonspecialists,"Diagnostics, clinical scenarios",39.00%,36,ChatGPT 4,ChatGPT-4,3/1/2023,"ChatGPT made a correct primary diagnosis 56% of the time (n = 20). Using the clinical history and cutaneous signs recorded by nonspecialists, it was able to make a correct diagnosis 39% of the time (n = 14). This was similar to the diagnostic rate of nonspecialists (36%; n = 13), but it was much lower than that of dermatologists (83%; n = 30). There was no differential offered by referring sources 28% of the time (n = 10), unlike ChatGPT, which provided a differential diagnosis 100% of the time. ",36 Medical records: Clinical information from 36 patients selected from 90 consecutive patients referred to a single dermatology emergency clinic between June and December 202,"Stoneham S, Livesey A, Cooper H, Mitchell C. ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):707-710. doi: 10.1093/ced/llad402. PMID: 37979201.",ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,9
1065,English,Primary Diagnosis from Clinical Records with clinical history and examination data obtained by a dermatologist,"Primary Diagnosis, Clinical scenarios",56.00%,36,ChatGPT 4,ChatGPT-4,3/1/2023,"ChatGPT made a correct primary diagnosis 56% of the time (n = 20). Using the clinical history and cutaneous signs recorded by nonspecialists, it was able to make a correct diagnosis 39% of the time (n = 14). This was similar to the diagnostic rate of nonspecialists (36%; n = 13), but it was much lower than that of dermatologists (83%; n = 30). There was no differential offered by referring sources 28% of the time (n = 10), unlike ChatGPT, which provided a differential diagnosis 100% of the time. ",36 Medical records: Clinical information from 36 patients selected from 90 consecutive patients referred to a single dermatology emergency clinic between June and December 202,"Stoneham S, Livesey A, Cooper H, Mitchell C. ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):707-710. doi: 10.1093/ced/llad402. PMID: 37979201.",ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,9
1066,English,Differential diagnosis,Differential diagnosis,100.00%,36,ChatGPT 4,ChatGPT-4,3/1/2023,"ChatGPT made a correct primary diagnosis 56% of the time (n = 20). Using the clinical history and cutaneous signs recorded by nonspecialists, it was able to make a correct diagnosis 39% of the time (n = 14). This was similar to the diagnostic rate of nonspecialists (36%; n = 13), but it was much lower than that of dermatologists (83%; n = 30). There was no differential offered by referring sources 28% of the time (n = 10), unlike ChatGPT, which provided a differential diagnosis 100% of the time. ",36 Medical records: Clinical information from 36 patients selected from 90 consecutive patients referred to a single dermatology emergency clinic between June and December 202,"Stoneham S, Livesey A, Cooper H, Mitchell C. ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):707-710. doi: 10.1093/ced/llad402. PMID: 37979201.",ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,9
1067,English,"Dermapathology questions for board examination preparation from 2023, with images",Dermatopathology,22.20%,9,ChatGPT 4,"ChatGPT-4, December 2023",12/1/2023,Two questions (2023) and six questions (2024) were double coded as having both clinical and dermatopathology images,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1068,English,"Dermatology questions for board examination preparation from 2023, with images",Certification,47.80%,67,ChatGPT 4,"ChatGPT-4, December 2023",12/1/2023,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1069,English,"Clinical dermatology and dermoscopy questions for board examination preparation, with images","Images, Dermatology Certification",50.90%,57,ChatGPT 4,"ChatGPT-4, December 2023",12/1/2023,Two questions (2023) and six questions (2024) were double coded as having both clinical and dermatopathology images,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1070,English,"Dermapathology questions for board examination preparation, with images",Dermatopathology,58.80%,17,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,Two questions (2023) and six questions (2024) were double coded as having both clinical and dermatopathology images,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1071,English,"Clinical dermatology and dermoscopy questions for board examination preparation from 2024, with images","Images, Dermatology Certification",59.60%,57,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,Two questions (2023) and six questions (2024) were double coded as having both clinical and dermatopathology images,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1072,English,"Dermatology questions for board examination in 2023, with or without images",Certification,65.30%,150,ChatGPT 4,"ChatGPT-4, December 2023",12/1/2023,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1073,English,"Dermatology questions for board examination preparation, with images",Certification,65.30%,150,ChatGPT 4,"ChatGPT-4, December 2023",12/1/2023,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1074,English,"Dermatology questions for board examination preparation from 2024, with images",Certification,65.70%,67,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1075,English,"Dermapathology questions for board examination preparation from 2024, with images",Dermatopathology,66.70%,9,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,Two questions (2023) and six questions (2024) were double coded as having both clinical and dermatopathology images,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1076,English,"Dermatology questions for board examination preparation, with images",Certification,67.70%,133,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,"Of the aggregate 300 question data, ChatGPT answered 232 questions correctly (77.3%). ChatGPT performed significantly better with text-only questions than with questions that included images (85.2%(144/169)vs 67.7%(90/133),P<.001). Of image-based questions, ChatGPT performed better with clinical image questions than with dermatopathology questions (69.0%(78/133) vs. 58.8% (10/17),P=.40),but this difference was not statistically significant. performing above the 46thpercentile of PGY-4 question bank users","300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1077,English,"Dermatology questions for board examination preparation from 2023 and 2024, with images",Certification,67.70%,133,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1078,English,"Clinical dermatology and dermoscopy questions for board examination preparation, with images","Images, Dermatology Certification",69.00%,113,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,"Of the aggregate 300 question data, ChatGPT answered 232 questions correctly (77.3%). ChatGPT performed significantly better with text-only questions than with questions that included images (85.2%(144/169)vs 67.7%(90/133),P<.001). Of image-based questions, ChatGPT performed better with clinical image questions than with dermatopathology questions (69.0%(78/133) vs. 58.8% (10/17),P=.40),but this difference was not statistically significant. performing above the 46thpercentile of PGY-4 question bank users","300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1079,English,"Dermatology questions for board examination in 2024, with or without images",Certification,73.30%,150,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1080,English,"Dermatology questions for board examination, with or without images",Certification,77.30%,300,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,,"300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1081,English,"Dermatology questions for board examination preparation, no images",Certification,85.20%,169,ChatGPT 4,"ChatGPT-4, July 2024",7/1/2024,"Of the aggregate 300 question data, ChatGPT answered 232 questions correctly (77.3%). ChatGPT performed significantly better with text-only questions than with questions that included images (85.2%(144/169)vs 67.7%(90/133),P<.001). Of image-based questions, ChatGPT performed better with clinical image questions than with dermatopathology questions (69.0%(78/133) vs. 58.8% (10/17),P=.40),but this difference was not statistically significant. performing above the 46thpercentile of PGY-4 question bank users","300 dermatology-related questions. 150 multiple-choice questions from DermQbank inputted into ChatGPT in December 2023 and then again in July 2024. Of these, 83 were text-only questions and 67 had associated images. An additional 150 questions inputted in 2024 made a total of 300 different questions where 169 were text-only and 133 had associated images.","Smith L, Hanna R, Hatch L, Hanna K. Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions. SKIN The Journal of Cutaneous Medicine. 2024 Sep 15;8(5):1815-21.",Computer Vision Meets Large Language Models: Performance of ChatGPT 4.0 on Dermatology Boards-Style Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,29
1082,English,General Skin Cancer Information,Skin cancer,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"NCI FKG was found to be 8.8 (content is written at approximately an 8th to 9th-grade reading level accessible to a broad audience, including the general public with middle school education. C FKG = 12.8 The content is written at nearly a 13th-grade level, which corresponds to the first year of college, more suitable for audiences with higher education backgrounds.",13 questions about cancer that are common points of confusion among the public (per NCI web page “Common Cancer Myths and Misconceptions” ,"Skyler B Johnson, Andy J King, Echo L Warner, Sanjay Aneja, Benjamin H Kann, Carma L Bylund, Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information, JNCI Cancer Spectrum, Volume 7, Issue 2, April 2023, pkad015, https://doi.org/10.1093/jncics/pkad015",Using ChatGPT to evaluate cancer myths and misconceptions: artificial intelligence and cancer information,Patient Education Materials and Readability Studies,Patient Education,2023,OpenAI GPT series,55
1083,English,"Dermatology License questions, UK, CSE exam",Certification,85.00%,89,ChatGPT 4,ChatGPT-4,3/14/2023,"GPT-4, the most advanced large language model, exhibits remarkable accuracy - answering in excess of 85% of questions correctly, at a level that would likely be sufficient to pass the SCE exam",89 publicly available sample questions from the Dermatology specialty certificate examination.,"Shetty M, Ettlinger M, Lynch M. GPT-4, an artificial intelligence large language model, exhibits high levels of accuracy on dermatology specialty certificate exam questions. medRxiv. 2023:2023-07. medRxiv 2023.07.13.23292418; doi: https://doi.org/10.1101/2023.07.13.23292418","GPT-4, an artificial intelligence large language model, exhibits high levels of accuracy on dermatology specialty certificate exam questions",Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,37
1084,English,"Genetic, Biological, and Structural Differences in Skin of Color",Skin of color,25.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,subset of the 81 questions,"81 multiple-choice questions, excluding image-based queries curated by dermatologists from a series of the most recent CME articles from the Journal of American Academy of Dermatology ranging from July 2023 - September 2022 (simulated board certification examination)","Sher, ArielKahan, ShaniRoster, KatiePeacock, AnjelicaPereira, Frederick et al. (2024) 54250 Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions Journal of the American Academy of Dermatology, Volume 91, Issue 3, AB131",Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,28
1085,English,Ecchymosis due to Child Abuse and Neglect,Ecchymosis,30.00%,2,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,subset of the 81 questions,"81 multiple-choice questions, excluding image-based queries curated by dermatologists from a series of the most recent CME articles from the Journal of American Academy of Dermatology ranging from July 2023 - September 2022 (simulated board certification examination)","Sher, ArielKahan, ShaniRoster, KatiePeacock, AnjelicaPereira, Frederick et al. (2024) 54250 Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions Journal of the American Academy of Dermatology, Volume 91, Issue 3, AB131",Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,28
1086,English,Physiology-related Dermatology questions ,Physiology,51.00%,5,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"ChatGPT garnered a total score of 58.0% in the simulated board certification examination series. It exhibited a strong performance in pharmacology, with a 71% success rate, in contrast to a 51% score in physiology-related questions. Nonetheless, it demonstrated a pronounced weakness regarding questions concerning Genetic, Biological, and Structural Differences in Skin of Color, and Child Abuse and Neglect, with scores of 25% and 30%, respectively","81 multiple-choice questions, excluding image-based queries curated by dermatologists from a series of the most recent CME articles from the Journal of American Academy of Dermatology ranging from July 2023 - September 2022 (simulated board certification examination)","Sher, ArielKahan, ShaniRoster, KatiePeacock, AnjelicaPereira, Frederick et al. (2024) 54250 Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions Journal of the American Academy of Dermatology, Volume 91, Issue 3, AB131",Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,28
1087,English,"Continued Medical Education (CME) examination, simulated Board certification examination",Certification,58.00%,81,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"ChatGPT garnered a total score of 58.0% in the simulated board certification examination series. It exhibited a strong performance in pharmacology, with a 71% success rate, in contrast to a 51% score in physiology-related questions. Nonetheless, it demonstrated a pronounced weakness regarding questions concerning Genetic, Biological, and Structural Differences in Skin of Color, and Child Abuse and Neglect, with scores of 25% and 30%, respectively","81 multiple-choice questions, excluding image-based queries curated by dermatologists from a series of the most recent CME articles from the Journal of American Academy of Dermatology ranging from July 2023 - September 2022 (simulated board certification examination)","Sher, ArielKahan, ShaniRoster, KatiePeacock, AnjelicaPereira, Frederick et al. (2024) 54250 Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions Journal of the American Academy of Dermatology, Volume 91, Issue 3, AB131",Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,28
1088,English,Pharmacology for Dermatology Certification,"Pharmacology , Certification",71.00%,5,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,subset of the 81 questions,"81 multiple-choice questions, excluding image-based queries curated by dermatologists from a series of the most recent CME articles from the Journal of American Academy of Dermatology ranging from July 2023 - September 2022 (simulated board certification examination)","Sher, ArielKahan, ShaniRoster, KatiePeacock, AnjelicaPereira, Frederick et al. (2024) 54250 Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions Journal of the American Academy of Dermatology, Volume 91, Issue 3, AB131",Assessing the Performance of ChatGPT in Answering Dermatology Practice Questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,28
1089,English,"Dermatology Misinformation in Reddit's dermatology forums, Test Accuracy for ""Tanning""",Tanning,95.65%,221,Custom Ai,"Logistic regression (Bag-of-Words: Unigram, Bigram, Trigram)",7/17/2019,,"Hundreds of post; publicly available Reddit data, pulling from the forums r/Dermatology, r/essentialoils, and r/tanning from January 2018 to August 2019.","Sager MA, Kashyap AM, Tamminga M, Ravoori S, Callison-Burch C, Lipoff JB. Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR Dermatol. 2021 Sep 30;4(2):e20975. doi: 10.2196/20975. PMID: 37632809; PMCID: PMC10334965.",Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study,Patient Education Materials and Readability Studies,Patient Education,2021,NOT LLM,50
1090,English,"Dermatology Misinformation in Reddit's dermatology forums, Test Accuracy for ""Essential Oils""",Essential Oils,97.29%,221,Custom Ai,"Logistic regression (Bag-of-Words: Unigram, Bigram, Trigram)",7/17/2019,,"Hundreds of post; publicly available Reddit data, pulling from the forums r/Dermatology, r/essentialoils, and r/tanning from January 2018 to August 2019.","Sager MA, Kashyap AM, Tamminga M, Ravoori S, Callison-Burch C, Lipoff JB. Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR Dermatol. 2021 Sep 30;4(2):e20975. doi: 10.2196/20975. PMID: 37632809; PMCID: PMC10334965.",Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study,Patient Education Materials and Readability Studies,Patient Education,2021,NOT LLM,50
1091,English,"Dermatology Misinformation in Reddit's dermatology forums, Test Accuracy for ""Tanning""",Tanning,98.61%,221,Custom Ai,"XLNet, Pre-Trained and Fine-Tuned",7/1/2021,,"Hundreds of post; publicly available Reddit data, pulling from the forums r/Dermatology, r/essentialoils, and r/tanning from January 2018 to August 2019.","Sager MA, Kashyap AM, Tamminga M, Ravoori S, Callison-Burch C, Lipoff JB. Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR Dermatol. 2021 Sep 30;4(2):e20975. doi: 10.2196/20975. PMID: 37632809; PMCID: PMC10334965.",Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study,Patient Education Materials and Readability Studies,Patient Education,2021,"NOT LLM, XLNet",50
1092,English,"Dermatology Misinformation in Reddit's dermatology forums, Test Accuracy for ""Essential Oils""",Essential Oils,98.70%,221,Custom Ai,"XLNet, Pre-Trained and Fine-Tuned",7/1/2021,,"Hundreds of post; publicly available Reddit data, pulling from the forums r/Dermatology, r/essentialoils, and r/tanning from January 2018 to August 2019.","Sager MA, Kashyap AM, Tamminga M, Ravoori S, Callison-Burch C, Lipoff JB. Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR Dermatol. 2021 Sep 30;4(2):e20975. doi: 10.2196/20975. PMID: 37632809; PMCID: PMC10334965.",Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study,Patient Education Materials and Readability Studies,Patient Education,2021,"NOT LLM, XLNet",50
1093,English,"Dermatology Misinformation in Reddit's dermatology forums, Test Accuracy for ""Essential Oils""",Essential Oils,99.56%,221,Bert,"BERT, Pre-Trained and Fine-Tuned",7/1/2021,Bidirectional Encoder Representations from Transformers (BERT) - ,"Hundreds of post; publicly available Reddit data, pulling from the forums r/Dermatology, r/essentialoils, and r/tanning from January 2018 to August 2019.","Sager MA, Kashyap AM, Tamminga M, Ravoori S, Callison-Burch C, Lipoff JB. Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR Dermatol. 2021 Sep 30;4(2):e20975. doi: 10.2196/20975. PMID: 37632809; PMCID: PMC10334965.",Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study,Patient Education Materials and Readability Studies,Patient Education,2021,"NOT general purpose, Task-Specific after fine tuning: Google's Family of LLMs",50
1094,English,"Dermatology Misinformation in Reddit's dermatology forums, Test Accuracy for ""Tanning""",Tanning,100.00%,221,Bert,"BERT, Pre-Trained and Fine-Tuned",7/1/2021,,"Hundreds of post; publicly available Reddit data, pulling from the forums r/Dermatology, r/essentialoils, and r/tanning from January 2018 to August 2019.","Sager MA, Kashyap AM, Tamminga M, Ravoori S, Callison-Burch C, Lipoff JB. Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study. JMIR Dermatol. 2021 Sep 30;4(2):e20975. doi: 10.2196/20975. PMID: 37632809; PMCID: PMC10334965.",Identifying and Responding to Health Misinformation on Reddit Dermatology Forums With Artificially Intelligent Bots Using Natural Language Processing: Design and Evaluation Study,Patient Education Materials and Readability Studies,Patient Education,2021,"NOT general purpose, Task-Specific after fine tuning: Google's Family of LLMs",50
1095,English,Melanoma FAQs readability,Melanoma,59.30%,3,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,"Flesch Reading Ease score ranges from 0 to 100, with higher scores indicating easier readability. The AAD’s sunscreen FAQs and melanoma FAQs had Flesch Reading Ease scores of 60.9 (standard/average) and 56.2 (fairly difficult), respectively.","The accuracy score represents the mean score of 3 dermatology residents who assessed the educational materials using a numeric scale: 1 (not accurate), 2 (somewhat accurate), and 3 (accurate).","Roster K, Kann RB, Farabi B, Gronbeck C, Brownstone N, Lipner SR. Readability and Health Literacy Scores for ChatGPT-Generated Dermatology Public Education Materials: Cross-Sectional Analysis of Sunscreen and Melanoma Questions. JMIR Dermatol. 2024 Mar 6;7:e50163. doi: 10.2196/50163. PMID: 38446502; PMCID: PMC10955394.",Readability and Health Literacy Scores for ChatGPT-Generated Dermatology Public Education Materials: Cross-Sectional Analysis of Sunscreen and Melanoma Questions,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,4
1096,English,Sunscreen FAQs readability,Mohs surgery,82.00%,3,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,"Flesch Reading Ease score ranges from 0 to 100, with higher scores indicating easier readability. The AAD’s sunscreen FAQs and melanoma FAQs had Flesch Reading Ease scores of 60.9 (standard/average) and 56.2 (fairly difficult), respectively.","The accuracy score represents the mean score of 3 dermatology residents who assessed the educational materials using a numeric scale: 1 (not accurate), 2 (somewhat accurate), and 3 (accurate).","Roster K, Kann RB, Farabi B, Gronbeck C, Brownstone N, Lipner SR. Readability and Health Literacy Scores for ChatGPT-Generated Dermatology Public Education Materials: Cross-Sectional Analysis of Sunscreen and Melanoma Questions. JMIR Dermatol. 2024 Mar 6;7:e50163. doi: 10.2196/50163. PMID: 38446502; PMCID: PMC10955394.",Readability and Health Literacy Scores for ChatGPT-Generated Dermatology Public Education Materials: Cross-Sectional Analysis of Sunscreen and Melanoma Questions,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,4
1097,English,Dermatology medical records: Top diagnoses,Medical Records,37.50%,32,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,Top diagnoses of dermatology cases from educational materials scored for accuracy against top diagnoses created by dermatologists,"32 dermatologic clinical cases: Ten dermatology cases extracted from Clinical Advisor and 22 from McGraw Hill, reviewed by a dermatologist to ensure they could be reasonably answered without any images","Ravipati A, Pradeep T, Elman SA. The role of artificial intelligence in dermatology: the promising but limited accuracy of ChatGPT in diagnosing clinical scenarios. Int J Dermatol. 2023 Oct;62(10):e547-e548. doi: 10.1111/ijd.16746. Epub 2023 Jun 12. PMID: 37306147.",The role of artificial intelligence in dermatology: the promising but limited accuracy of ChatGPT in diagnosing clinical scenarios,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,3
1098,English,Dermatology medical records: Per-Case Inclusion/Differential diagnoses of dermatology cases from educational materials scored for accuracy against differential diagnoses created by dermatologists (the correct diagnosis is listed somewhere in its differential diagnosis ),Medical Records,81.00%,32,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,"32 dermatologic clinical cases: Ten dermatology cases extracted from Clinical Advisor and 22 from McGraw Hill, reviewed by a dermatologist to ensure they could be reasonably answered without any images","Ravipati A, Pradeep T, Elman SA. The role of artificial intelligence in dermatology: the promising but limited accuracy of ChatGPT in diagnosing clinical scenarios. Int J Dermatol. 2023 Oct;62(10):e547-e548. doi: 10.1111/ijd.16746. Epub 2023 Jun 12. PMID: 37306147.",The role of artificial intelligence in dermatology: the promising but limited accuracy of ChatGPT in diagnosing clinical scenarios,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,3
1099,English,Dx: Skin of color case reports involving histopathology,Skin of color,67.70%,12,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT could predict dermatoses in people with lighter and darker skin color with similar levels of accuracy. A notable finding was that GPT-3.5's accuracy was shown to decrease as additional clinical data was added, yet GPT-4's accuracy improved when additional data was included. A correct diagnosis occurred when using GPT-4 in 100% of the skin of color cases involving histopathology, and this contrasted with an accuracy rate of 66.7% for GPT-3.","29 cases in total, 14 from a general dermatology textbook assigned to patients in the non-skin of color group. 15 from a dermatology textbook which was aimed at skin of color and then assigned to this group during the study. Twelve of the skin of color cases involved histopathological reports. A correct diagnosis occurred when using GPT-4 in 100% of the skin of color cases involving histopathology, and this contrasted with an accuracy rate of 66.7% for GPT-3. However, this distinction was not reported by the research team to be statistically significant (P?=?.093). The histories and physical assessment details of each case were inputted by the investigators into the GPT-3.5 and GPT-4 models and used medical terminology, with questions posed to the AI to identify the top 3 differential diagnoses.","Qureshi S, Alli SR, Ogunyemi B (2024). Accuracy of ChatGPT-3.5 and GPT-4 in diagnosing clinical scenarios in dermatology involving skin of color. Int J Dermatol. https://doi.org/10.1111/ijd.17425.",Accuracy of ChatGPT�3.5 and GPT�4 in diagnosing clinical scenarios in dermatology involving skin of color,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,25
1100,English,Dx: Skin of color case reports involving histopathology,Skin of color,100.00%,12,ChatGPT 4,ChatGPT-4,3/14/2023,"ChatGPT could predict dermatoses in people with lighter and darker skin color with similar levels of accuracy. A notable finding was that GPT-3.5's accuracy was shown to decrease as additional clinical data was added, yet GPT-4's accuracy improved when additional data was included. A correct diagnosis occurred when using GPT-4 in 100% of the skin of color cases involving histopathology, and this contrasted with an accuracy rate of 66.7% for GPT-3.","29 cases in total, 14 from a general dermatology textbook assigned to patients in the non-skin of color group. 15 from a dermatology textbook which was aimed at skin of color and then assigned to this group during the study. Twelve of the skin of color cases involved histopathological reports. A correct diagnosis occurred when using GPT-4 in 100% of the skin of color cases involving histopathology, and this contrasted with an accuracy rate of 66.7% for GPT-3. However, this distinction was not reported by the research team to be statistically significant (P?=?.093). The histories and physical assessment details of each case were inputted by the investigators into the GPT-3.5 and GPT-4 models and used medical terminology, with questions posed to the AI to identify the top 3 differential diagnoses.","Qureshi S, Alli SR, Ogunyemi B (2024). Accuracy of ChatGPT-3.5 and GPT-4 in diagnosing clinical scenarios in dermatology involving skin of color. Int J Dermatol. https://doi.org/10.1111/ijd.17425.",Accuracy of ChatGPT�3.5 and GPT�4 in diagnosing clinical scenarios in dermatology involving skin of color,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,25
1101,English,Viral infection,Viral infection,26.70%,398,Custom Ai,Tibot AI (NOT an LLM ),7/1/2020,"The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. No skin types were recorded in this study, but it was generally between Fitzpatrick types four to six.","Descriptive Data of 398 patients of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). ","Patil S, Rao ND, Patil A, Basar F, Bate S. Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study. Indian Dermatol Online J. 2020;11(6):910–4",Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study,Medical Records and Diagnostic Processes,Clinical Practice,2020,"NOT LLM, TibotAI",N/A
1102,English,Infestation,Infestation,59.30%,398,Custom Ai,Tibot AI (NOT an LLM ),7/1/2020,"The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. No skin types were recorded in this study, but it was generally between Fitzpatrick types four to six.","Descriptive Data of 398 patients of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). ","Patil S, Rao ND, Patil A, Basar F, Bate S. Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study. Indian Dermatol Online J. 2020;11(6):910–4",Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study,Medical Records and Diagnostic Processes,Clinical Practice,2020,"NOT LLM, TibotAI",N/A
1103,English,Benign tumors,Benign tumors,71.40%,398,Custom Ai,Tibot AI (NOT an LLM ),7/1/2020,"The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. No skin types were recorded in this study, but it was generally between Fitzpatrick types four to six.","Descriptive Data of 398 patients of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). ","Patil S, Rao ND, Patil A, Basar F, Bate S. Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study. Indian Dermatol Online J. 2020;11(6):910–4",Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study,Medical Records and Diagnostic Processes,Clinical Practice,2020,"NOT LLM, TibotAI",N/A
1104,English,Eczema,Eczema,91.70%,398,Custom Ai,Tibot AI (NOT an LLM ),7/1/2020,"The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. No skin types were recorded in this study, but it was generally between Fitzpatrick types four to six.","Descriptive Data of 398 patients of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). ","Patil S, Rao ND, Patil A, Basar F, Bate S. Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study. Indian Dermatol Online J. 2020;11(6):910–4",Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study,Medical Records and Diagnostic Processes,Clinical Practice,2020,"NOT LLM, TibotAI",N/A
1105,English,Fungal infections,Fungal infections,95.60%,398,Custom Ai,Tibot AI (NOT an LLM ),7/1/2020,"The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. No skin types were recorded in this study, but it was generally between Fitzpatrick types four to six.","Descriptive Data of 398 patients of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). ","Patil S, Rao ND, Patil A, Basar F, Bate S. Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study. Indian Dermatol Online J. 2020;11(6):910–4",Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study,Medical Records and Diagnostic Processes,Clinical Practice,2020,"NOT LLM, TibotAI",N/A
1106,English,Alopecia,Alopecia,100.00%,398,Custom Ai,Tibot AI (NOT an LLM ),7/1/2020,"The prediction accuracy (ability to get diagnosis in top three conditions) for alopecia, fungal infections, and eczema was 100%, 95.6%, and 91.7%, respectively. Mean prediction accuracy for correct diagnosis in the predicted top three diagnoses was 85.2%, and for correct diagnosis was 60.7%. Sensitivity and specificity of the application were approximately 86% and 98%, respectively. The sensitivity and positive predictive value of the application to diagnose alopecia was 100% and for fungal infections it was 96.85% and 90.05%, respectively. No skin types were recorded in this study, but it was generally between Fitzpatrick types four to six.","Descriptive Data of 398 patients of whom 159 (39.9%) had fungal infections. Other conditions included eczema 36 (9%), alopecia 28 (7%), infestations 27 (6.8%), acne 25 (6.3%), psoriasis 19 (4.8%), benign tumors 7 (1.8%), bacterial infection 19 (4.8%), viral infection 15 (3.8%), and pigmentary disorders 20 (5%). ","Patil S, Rao ND, Patil A, Basar F, Bate S. Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study. Indian Dermatol Online J. 2020;11(6):910–4",Assessment of Tibot(R) artificial intelligence application in prediction of diagnosis in dermatological conditions: results of a single centre study,Medical Records and Diagnostic Processes,Clinical Practice,2020,"NOT LLM, TibotAI",N/A
1107,English,Dermatology Specialty Certificate Examination (SCE),Certification,63.00%,84,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,The typical pass mark for the dermatology SCE is 70-72%.,84 multiple-choice sample questions from the sample SCE in Dermatology question bank,"Passby L, Jenko N, Wernham A. Performance of ChatGPT on Specialty Certificate Examination in Dermatology multiple-choice questions. Clin Exp Dermatol. 2024 Jun 25;49(7):722-727. doi: 10.1093/ced/llad197. PMID: 37264670.",Performance of ChatGPT on Specialty Certificate Examination in Dermatology multiple-choice questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,2
1108,English,Dermatology Specialty Certificate Examination (SCE),Certification,90.00%,84,ChatGPT 4,ChatGPT-4,3/1/2023,The typical pass mark for the dermatology SCE is 70-72%.,84 multiple-choice sample questions from the sample SCE in Dermatology question bank,"Passby L, Jenko N, Wernham A. Performance of ChatGPT on Specialty Certificate Examination in Dermatology multiple-choice questions. Clin Exp Dermatol. 2024 Jun 25;49(7):722-727. doi: 10.1093/ced/llad197. PMID: 37264670.",Performance of ChatGPT on Specialty Certificate Examination in Dermatology multiple-choice questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,2
1109,English,"Skin tone 10 (NIS scale) images -  approximately corresponds to  FST VI (very dark, deeply pigmented skin)","Dark skin, pigmented skin",0.00%,100,ChatGPT 4,Custom GPT for Skin Tones,11/1/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1110,English,Skin tone 5 (NIS scale) images -  approximately corresponds to FST IV (olive or medium-dark skin tones).,Medium Skin,0.00%,96,Dall-E,Dall-E,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1111,English,Skin tone 6 (NIS scale) images -  approximately corresponds to FST IV (olive or medium-dark skin tones).,Olive skin,0.00%,96,Dall-E,Dall-E,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1112,English,Skin tone 7 (NIS scale) images -  approximately corresponds to  FST V-VI,Dark skin,0.00%,96,Dall-E,Dall-E,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1113,English,"Skin tone 9 (NIS scale) images -  approximately corresponds to  FST V-VI (dark brown skin with warm, neutral, or occasionally cooler undertones)",Dark skin,0.00%,96,Dall-E,Dall-E,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1114,English,"Skin tone 10 (NIS scale) images -  approximately corresponds to  FST VI (very dark, deeply pigmented skin)","Dark skin, pigmented skin",0.00%,96,Dall-E,Dall-E,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1115,English,Skin tone 4 (NIS scale) images -  approximately corresponds to FST III (medium skin tones).,Medium Skin,0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1116,English,Skin tone 5 (NIS scale) images -  approximately corresponds to FST IV (olive or medium-dark skin tones).,Medium Skin,0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1117,English,Skin tone 6 (NIS scale) images -  approximately corresponds to FST IV (olive or medium-dark skin tones).,Olive skin,0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1118,English,Skin tone 7 (NIS scale) images -  approximately corresponds to  FST V-VI,Dark skin,0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1119,English,Skin tone 8 (NIS scale) images -  approximately corresponds to  FST V (dark brown),Dark skin,0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1120,English,"Skin tone 9 (NIS scale) images -  approximately corresponds to  FST V-VI (dark brown skin with warm, neutral, or occasionally cooler undertones)",Dark skin,0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1121,English,"Skin tone 10 (NIS scale) images -  approximately corresponds to  FST VI (very dark, deeply pigmented skin)","Dark skin, pigmented skin",0.00%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1122,English,Skin tone 1 (NIS scale) images -  approximately corresponds to FST I-II (lighter skin tones).,Light Skin,0.95%,96,ChatGPT 4,GPT paired with it to improve prompt interpretation and safety,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1123,English,Skin tone 3 (NIS scale) images -  approximately corresponds to FST III (medium skin tones).,Fitzpatrick III skin type,6.87%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1124,English,"Skin tone 9 (NIS scale) images -  approximately corresponds to  FST V-VI (dark brown skin with warm, neutral, or occasionally cooler undertones)",Skin of color,19.28%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1125,English,Skin tone 8 (NIS scale) images -  approximately corresponds to  FST V (dark brown),Skin of color,19.34%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1126,English,Skin tone 8 (NIS scale) images -  approximately corresponds to  FST V (dark brown),Skin of color,20.16%,96,ChatGPT 4,GPT paired with it to improve prompt interpretation and safety,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1127,English,Skin tone 2 (NIS scale) images -  approximately corresponds to FST I-II (lighter skin tones).,Skin of color,20.16%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1128,English,Skin tone 2 (NIS scale) images -  approximately corresponds to FST I-II (lighter skin tones).,Skin of color,23.39%,96,ChatGPT 4,GPT paired with it to improve prompt interpretation and safety,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1129,English,Skin tone 4 (NIS scale) images -  approximately corresponds to FST III (medium skin tones).,Skin of color,27.94%,96,ChatGPT 4,GPT paired with it to improve prompt interpretation and safety,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1130,English,Skin tone 6 (NIS scale) images -  approximately corresponds to FST IV (olive or medium-dark skin tones).,Skin of color,46.01%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1131,English,Skin tone 4 (NIS scale) images -  approximately corresponds to FST III (medium skin tones).,Skin of color,53.69%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1132,English,Skin tone 1 (NIS scale) images -  approximately corresponds to FST I-II (lighter skin tones).,Skin of color,56.97%,99,Midjourney,Midjourney,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,Midjourney,39
1133,English,Skin tone 1 (NIS scale) images -  approximately corresponds to FST I-II (lighter skin tones).,Skin of color,59.09%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1134,English,Skin tone 7 (NIS scale) images -  approximately corresponds to  FST V-VI,Skin of color,61.82%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1135,English,Skin tone 5 (NIS scale) images -  approximately corresponds to FST IV (olive or medium-dark skin tones).,Skin of color,64.77%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1136,English,Skin tone 3 (NIS scale) images -  approximately corresponds to FST III (medium skin tones).,Skin of color,70.75%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1137,English,Skin tone 3 (NIS scale) images -  approximately corresponds to FST III (medium skin tones).,Skin of color,71.01%,96,Dall-E,Dall-E,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1138,English,Skin tone 2 (NIS scale) images -  approximately corresponds to FST I-II (lighter skin tones).,Skin of color,81.36%,100,Custom Ai,Custom GPT for Skin Tones,8/20/2023,"The range of skin tones present in the images generated by both Dall-E 3 and Midjourney are significantly different to what would be expected in the US population.  CustomGPT shows a better result. Custom GPT Model with prompt injection closely aligns with the expected demographic distribution, evidenced by a lower Chi-Square statistic (17.35) and a non-significant p-value (P = .0435), indicating minimal deviation from the target distribution. The New Immigrant Survey (NIS) skin tone scale is a standardized scale used to classify and assess skin tone, primarily for research on social and demographic factors. The scale ranges from 1 to 10, where each score represents a gradation of skin tone, with 1 being the lightest skin tone and 10 being the darkest. Validity is computed as Goodness of Fit Percentage estimated based on Standardized Residuals","100 images of people with psoriasis generated by two standard AI models (Dall-E and Midjourney). Additionally, a custom model was developed which incorporated a prompt injection aimed at “forcing” the AI (Dall-E 3) to reflect the skin tone distribution of the US population according to the 2012 American National Election Survey. This custom model generated another set of 100 images. The skin tones in these images were assessed by three researchers using the New Immigrant Survey skin tone scale, with the median value representing each image. ","O'Malley A, Veenhuizen M, Ahmed A. Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias. JMIR AI. 2024 Nov 27;3:e58275. doi: 10.2196/58275. PMID: 39602221; PMCID: PMC11635324. // was   Ensuring appropriate representation in AI-generated medical imagery: A Methodological Approach to Address Skin Tone Bias (Preprint) DOI: 10.2196/58275 URL: https://preprints.jmir.org/preprint/58275  ",Ensuring Appropriate Representation in Artificial Intelligence-Generated Medical Imagery: Protocol for a Methodological Approach to Address Skin Tone Bias.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,39
1139,English,"Dermatology questions included into set of 45 general questions; relative readability, Flesch Reading Ease (FRES)",Readability,17.67%,45,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1140,English,"Dermatology questions included into set of 45 general questions; relative readability, Flesch Reading Ease (FRES)",Readability,39.34%,45,Bard,Bard,3/21/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,46
1141,English,"Dermatology questions included into set of 45 general questions; relative readability, Gunning Fog Scale (GFI) as percentage vs easy to read (10)",Readability,50.68%,45,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1142,English,"Dermatology questions included into set of 45 general questions; relative readability, Flesch-Kincaid Grade Level (FKGL) as percentage vs appropriatness for high school (10)",Readability,62.62%,45,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1143,English,"Dermatology questions included into set of 45 general questions; relative readability, Dale-Chall Score (DCRF) as percentage vs easy to read (7.5)",Readability,63.18%,45,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1144,English,"Dermatology questions included into set of 45 general questions; relative readability, Gunning Fog Scale (GFI) as percentage vs easy to read (10)",Readability,63.40%,45,Bard,Bard,3/21/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,46
1145,English,"Dermatology questions included into set of 45 general questions; relative readability, Flesch-Kincaid Grade Level (FKGL) as percentage vs appropriatness for high school (10)",Readability,71.32%,45,Piai,PiAI,11/15/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,PiAI,46
1146,English,"Dermatology questions included into set of 45 general questions; relative readability, Dale-Chall Score (DCRF) as percentage vs easy to read (7.5)",Readability,73.24%,45,Bard,Bard,3/21/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,46
1147,English,Dermatology questions included into set of 45 general questions; percent valid response,Readability,100.00%,45,ChatGPT 3.5,ChatGPT 3.5 Turbo,11/15/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1148,English,Dermatology questions included into set of 45 general questions; percent valid response,Readability,100.00%,45,Copilot,Copilot,11/15/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1149,English,Dermatology questions included into set of 45 general questions; percent valid response,Readability,100.00%,45,Piai,PiAI,11/15/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,PiAI,46
1150,English,Dermatology questions included into set of 45 general questions; percent valid response,Readability,Less than 100%,45,Bard,Bard,3/21/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,46
1151,English,Dermatology questions included into set of 45 general questions; percent valid response,Readability,Less than 100%,45,ChatGPT 3.5,ChatSpot (GPT-3.5),11/15/2023,"Only three chatbots (Microsoft Copilot, PiAI, and ChatGPT) responded to all 45 questions. In terms of readability, the Flesch Reading Ease Scale scores ranged from 17.67 (ChatGPT) to 39.34 (Bard), indicating the relative complexity of the responses. The Flesch-Kincaid Grade Level, which reflects the academic grade level required to comprehend the text, ranged from 14.02 (PiAI) to 15.97 (ChatGPT). The Gunning Fog Scale Level, another measure of readability, varied from 15.77 (Bard) to 19.73 (ChatGPT). Lastly, the Dale-Chall Score, which assesses the understandability of the text, ranged from 10.24 (Bard) to 11.87 (ChatGPT).","45 questions asked, the responses generated by the chatbots were then compared against established guidelines from the European Society of Cardiology, American Academy of Dermatology, and American Society of Clinical Oncology. In addition to the content, the readability of the responses was evaluated using four different readability scales: the Flesch Reading Scale, Gunning Fog Scale Level, Flesch-Kincaid Grade Level, and Dale-Chall Score.","Olszewski R, Brzezinski J, Watros K, Manczak M, Owoc J, Jeziorski K. Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages. European Heart Journal. 2024 Oct;45(Supplement_1):ehae666-3495.",Exploring the role of AI-driven chatbots in patient care: a critical evaluation amidst healthcare staff shortages,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,46
1152,English,DPTE; Location: Buttock,DPTE,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 3; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1153,English,EMPD; Location: Perineum,EMPD,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 8; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1154,English,Leiomyosarcoma; Location: Hand,Leiomyosarcoma,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 8; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1155,English,Melanoma; Location: Chin,Melanoma,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 7; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1156,English,MCC; Location: Cheek,MCC,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 7; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1157,English,SCC; Location: Neck,SCC,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 8; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1158,English,SCC; Location: Eyebrow,SCC,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 3; Decision: Incorrect,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1159,English,Angiosarcoma; Location: Lip,Angiosarcoma,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 5; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1160,English,BCC; Location: Forearm,BCC,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 6; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1161,English,LMM; Location: Trunk,LMM,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 4; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1162,English,LMM; Location: Helix,LMM,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: NA; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1163,English,LMM; Location: Foot,LMM,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: NA; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1164,English,Melanoma; Location: Shoulder,Melanoma,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 5; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1165,English,Melanoma; Location: Cheek,Melanoma,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: NA; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1166,English,SCC; Location: Thigh,SCC,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 6; Decision: Not Assessed,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1167,English,"Cutaneous tumors, treatment, surgery, appropriatness of wide local excision (WLE) vs Mohs surgery (MS) recommendation",Cutaneous tumors,68.00%,30,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"ChatGPT demonstrated 68% (n = 15) congruence with the MS AUC when triaging surgical management of 22 clinical scenarios characterized as clearly appropriate or inappropriate by the MS AUC. For all 5 cases characterized as indeterminate by the MS AUC, ChatGPT recommended against MS. For 3 cases of invasive melanoma, ChatGPT recommended MS for lentigo maligna melanoma of the helix, while recommending WLE for superficial spreading melanoma of the cheek and lentigo maligna melanoma of the dorsal foot.","30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1168,English,AFX; Location: Neck,AFX,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 8; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1169,English,BCC; Location: Eyelid,BCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 9; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1170,English,BCC; Location: Scalp,BCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 9; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1171,English,BCC; Location: Nose,BCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 7; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1172,English,BCC; Location: Back,BCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 1; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1173,English,Bowenoid Papules; Location: Penis,Bowenoid Papules,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 3; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1174,English,DFSP; Location: Upper Arm,DFSP,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 9; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1175,English,DFSP; Location: Upper Arm,DFSP,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 9; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1176,English,LM; Location: Trunk,LMM,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 7; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1177,English,LM; Location: Helix,LMM,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 8; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1178,English,SCC; Location: Shoulder,SCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 8; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1179,English,SCC; Location: Neck,SCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 7; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1180,English,SCC; Location: Shoulder,SCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 7; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1181,English,SCC; Location: Abdomen,SCC,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 3; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1182,English,Sebaceous Carcinoma; Location: Eyebrow,Sebaceous Carcinoma,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,AUC: 9; Decision: Correct,"30 clinical scenarios involving common and rare cutaneous tumors across anatomic sites presented to ChatGPT to determine whether wide local excision (WLE) or Mohs surgery (MS) were appropriate treatment options, correlating recommendations with MS appropriate use criteria (AUC)","O'Hern K, Yang E, Vidal NY. ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms. JAAD Int. 2023 Jun 9;12:168-170. doi: 10.1016/j.jdin.2023.06.002. PMID: 37404248; PMCID: PMC10316650.",ChatGPT underperforms in triaging appropriate use of Mohs surgery for cutaneous neoplasms,Medical Records and Diagnostic Processes,Clinical Practice,2023,OpenAI GPT series,52
1183,English,Non-Melanoma Identification Accuracy  from Images of Skin Lesions,Non-melanoma,6.56%,899,ChatGPT 4o,ChatGPT-4 Omni,5/13/2024,,"1000 images, 500 images each of melanoma and non-melanoma randomly selected.","Chetla et al: Nitin Chetla, Joseph Chang, Samantha Sattler, Matthew Chen, William Young Guo, Dia Shah, Jeremy Hugh (2024) What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? JMIR Dermatology (Preprint) 14/10/2024:67551DOI: 10.2196/preprints.67551URL: https://preprints.jmir.org/preprint/67551; now published as Sattler SS, Chetla N, Chen M, Hage TR, Chang J, Guo WY, Hugh J. Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study. JMIR Dermatol. 2025 Mar 21;8:e67551. doi: 10.2196/67551. PMID: 40117499; PMCID: PMC11952272.",What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,31
1184,English,Non-Melanoma Identification Accuracy from Images of Skin Lesions   - Binary prompt,Non-melanoma,25.25%,998,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"1000 images, 500 images each of melanoma and non-melanoma randomly selected.","Chetla et al: Nitin Chetla, Joseph Chang, Samantha Sattler, Matthew Chen, William Young Guo, Dia Shah, Jeremy Hugh (2024) What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? JMIR Dermatology (Preprint) 14/10/2024:67551DOI: 10.2196/preprints.67551URL: https://preprints.jmir.org/preprint/67551; now published as Sattler SS, Chetla N, Chen M, Hage TR, Chang J, Guo WY, Hugh J. Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study. JMIR Dermatol. 2025 Mar 21;8:e67551. doi: 10.2196/67551. PMID: 40117499; PMCID: PMC11952272.",What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,31
1185,English,Melanoma Identification Accuracy from Images of Skin Lesions,Melanoma,54.60%,1000,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,,"1000 images, 500 images each of melanoma and non-melanoma randomly selected.","Chetla et al: Nitin Chetla, Joseph Chang, Samantha Sattler, Matthew Chen, William Young Guo, Dia Shah, Jeremy Hugh (2024) What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? JMIR Dermatology (Preprint) 14/10/2024:67551DOI: 10.2196/preprints.67551URL: https://preprints.jmir.org/preprint/67551; now published as Sattler SS, Chetla N, Chen M, Hage TR, Chang J, Guo WY, Hugh J. Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study. JMIR Dermatol. 2025 Mar 21;8:e67551. doi: 10.2196/67551. PMID: 40117499; PMCID: PMC11952272.",What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,31
1186,English,Melanoma Identification Accuracy from Images of Skin Lesions,Melanoma,57.70%,1000,ChatGPT 4o,ChatGPT-4 Omni,5/13/2024,,"1000 images, 500 images each of melanoma and non-melanoma randomly selected.","Chetla et al: Nitin Chetla, Joseph Chang, Samantha Sattler, Matthew Chen, William Young Guo, Dia Shah, Jeremy Hugh (2024) What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? JMIR Dermatology (Preprint) 14/10/2024:67551DOI: 10.2196/preprints.67551URL: https://preprints.jmir.org/preprint/67551; now published as Sattler SS, Chetla N, Chen M, Hage TR, Chang J, Guo WY, Hugh J. Evaluating the Diagnostic Accuracy of ChatGPT-4 Omni and ChatGPT-4 Turbo in Identifying Melanoma: Comparative Study. JMIR Dermatol. 2025 Mar 21;8:e67551. doi: 10.2196/67551. PMID: 40117499; PMCID: PMC11952272.",What is the Capability and Accuracy of ChatGPT’s Newest Models in Diagnosing between Melanoma and Non-Melanoma lesions? (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,31
1187,English,"Basal cell carcinoma, image-based diagnosis, differential diagnosis (top 10)",Basal Cell Carcinoma,90.00%,54,Custom Ai,"Custom Deep Learning Algorithm, Vision Transformers",1/17/2021,Malignant lesions (basal cell carcinoma and squamous cell carcinoma) were identified within the top-5 most likely diagnoses in above 90% of cases. ,"135 patients' images, 92 (68.1%) were male and 43 (31.9%) were female. The median age was 71 years +/- 9 (Min: 56/Max: 91). Of those, 108 were malignant pathologies (54 basal cell carcinoma and 54 squamous cell carcinoma) and 27 benign pathologies (14 seborrheic keratoses, 2 actinic keratoses, and 11 actinic cheilitis)","Medela A, Sabater A, Montilla IH, MacCarthy T, Aguilar A, Chiesa-Estomba CM. The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies. Eur Arch Otorhinolaryngol. 2024 Sep 6. doi: 10.1007/s00405-024-08951-z. Epub ahead of print. PMID: 39242415.",The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies,Medical Records and Diagnostic Processes,Clinical Practice,2024,"NOT LLM, Transformer",47
1188,English,"Squamous cell carcinoma, image-based diagnosis, differential diagnosis (top 10)",Squamous Cell Carcinoma,90.00%,54,Custom Ai,"Custom Deep Learning Algorithm, Vision Transformers",1/17/2021,Malignant lesions (basal cell carcinoma and squamous cell carcinoma) were identified within the top-5 most likely diagnoses in above 90% of cases. ,"135 patients' images, 92 (68.1%) were male and 43 (31.9%) were female. The median age was 71 years +/- 9 (Min: 56/Max: 91). Of those, 108 were malignant pathologies (54 basal cell carcinoma and 54 squamous cell carcinoma) and 27 benign pathologies (14 seborrheic keratoses, 2 actinic keratoses, and 11 actinic cheilitis)","Medela A, Sabater A, Montilla IH, MacCarthy T, Aguilar A, Chiesa-Estomba CM. The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies. Eur Arch Otorhinolaryngol. 2024 Sep 6. doi: 10.1007/s00405-024-08951-z. Epub ahead of print. PMID: 39242415.",The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies,Medical Records and Diagnostic Processes,Clinical Practice,2024,"NOT LLM, Transformer",47
1189,English,Not confabulated Immunohistochemistry references in dermatopathology,Immunochemistry,72.80%,51,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,"generating immunohistochemical panels/immunophenotypes of 51 cutaneous diseases, including a diverse variety of epidermal, adnexal, hematolymphoid, and soft tissue entities. ","McCrary MR, Galambus J, Chen WS. Evaluating the diagnostic performance of a large language model-powered chatbot for providing immunohistochemistry recommendations in dermatopathology. J Cutan Pathol. 2024 Sep;51(9):689-695. doi: 10.1111/cup.14631. Epub 2024 May 14. PMID: 38744501.",Evaluating the diagnostic performance of a large language model�powered chatbot for providing immunohistochemistry recommendations in dermatopathology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,17
1190,English,Clinical usefulness of Immunohistochemistry recommendations in dermatopathology,Dermatopathology,75.50%,51,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,generating immunohistochemical panels for dermatologic diagnoses.,"McCrary MR, Galambus J, Chen WS. Evaluating the diagnostic performance of a large language model-powered chatbot for providing immunohistochemistry recommendations in dermatopathology. J Cutan Pathol. 2024 Sep;51(9):689-695. doi: 10.1111/cup.14631. Epub 2024 May 14. PMID: 38744501.",Evaluating the diagnostic performance of a large language model�powered chatbot for providing immunohistochemistry recommendations in dermatopathology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,17
1191,English,Immunohistochemistry recommendations in dermatopathology: factual correctness,"Immunohistochemistry, dermatopathology",86.10%,51,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,generating immunohistochemical panels for dermatologic diagnoses.,"McCrary MR, Galambus J, Chen WS. Evaluating the diagnostic performance of a large language model-powered chatbot for providing immunohistochemistry recommendations in dermatopathology. J Cutan Pathol. 2024 Sep;51(9):689-695. doi: 10.1111/cup.14631. Epub 2024 May 14. PMID: 38744501.",Evaluating the diagnostic performance of a large language model�powered chatbot for providing immunohistochemistry recommendations in dermatopathology,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,17
1192,English,Diagnostics from text-based clinical case scenarios pertaining to different dermatological conditions,"Diagnostics, clinical scenarios",90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,Ten clinical case scenarios pertaining to different dermatological conditions from a publicly available website:  https://www.clinicaladvisor.com/home/dermatology-clinic/.,"Manoharan P, Surapaneni KM. Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology. Indian J Dermatol Venereol Leprol. 2024 May 25:1-3. doi: 10.25259/IJDVL_1267_2023. Epub ahead of print. PMID: 38841923.",Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,18
1193,English,Diagnostics from text-based clinical case scenarios pertaining to different dermatological conditions,"Diagnostics, clinical scenarios",90.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,Ten clinical case scenarios pertaining to different dermatological conditions from a publicly available website:  https://www.clinicaladvisor.com/home/dermatology-clinic/.,"Manoharan P, Surapaneni KM. Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology. Indian J Dermatol Venereol Leprol. 2024 May 25:1-3. doi: 10.25259/IJDVL_1267_2023. Epub ahead of print. PMID: 38841923.",Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,18
1194,English,Clinical-based scenario,Poroma,FAIL,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"""ChatGPT 3.5 has generated the wrong answer for the case scenario on Firm Round Papule on Foot. The answer generated by ChatGPT was ‘Verruca vulgaris’ whereas the answer was ‘Poroma’ in the key. But ChatGPT 4.0 has rightly diagnosed this case. In the case of a ‘swelling on the knee’, ChatGPT 3.5 rightly diagnosed the condition as a ganglionic cyst, whereas ChatGPT 4.0 diagnosed it to be a lipoma. The main discrepancy in the explanations generated was based on the typical features of lipoma and ganglionic cysts. While ChatGPT 3.5 has considered the location of joints to be the most common site for ganglionic cysts, ChatGPT 4.0 has given the diagnosis based on size and skin over the bump""",Ten clinical case scenarios pertaining to different dermatological conditions from a publicly available website:  https://www.clinicaladvisor.com/home/dermatology-clinic/.,"Manoharan P, Surapaneni KM. Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology. Indian J Dermatol Venereol Leprol. 2024 May 25:1-3. doi: 10.25259/IJDVL_1267_2023. Epub ahead of print. PMID: 38841923.",Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,18
1195,English,Clinical-based scenario,Ganglionic cyst,FAIL,1,ChatGPT 4,ChatGPT-4,3/14/2023,"""ChatGPT 3.5 has generated the wrong answer for the case scenario on Firm Round Papule on Foot. The answer generated by ChatGPT was ‘Verruca vulgaris’ whereas the answer was ‘Poroma’ in the key. But ChatGPT 4.0 has rightly diagnosed this case. In the case of a ‘swelling on the knee’, ChatGPT 3.5 rightly diagnosed the condition as a ganglionic cyst, whereas ChatGPT 4.0 diagnosed it to be a lipoma. The main discrepancy in the explanations generated was based on the typical features of lipoma and ganglionic cysts. While ChatGPT 3.5 has considered the location of joints to be the most common site for ganglionic cysts, ChatGPT 4.0 has given the diagnosis based on size and skin over the bump""",Ten clinical case scenarios pertaining to different dermatological conditions from a publicly available website:  https://www.clinicaladvisor.com/home/dermatology-clinic/.,"Manoharan P, Surapaneni KM. Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology. Indian J Dermatol Venereol Leprol. 2024 May 25:1-3. doi: 10.25259/IJDVL_1267_2023. Epub ahead of print. PMID: 38841923.",Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,18
1196,English,Clinical-based scenario,Ganglionic cyst,PASS,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"""ChatGPT 3.5 has generated the wrong answer for the case scenario on Firm Round Papule on Foot. The answer generated by ChatGPT was ‘Verruca vulgaris’ whereas the answer was ‘Poroma’ in the key. But ChatGPT 4.0 has rightly diagnosed this case. In the case of a ‘swelling on the knee’, ChatGPT 3.5 rightly diagnosed the condition as a ganglionic cyst, whereas ChatGPT 4.0 diagnosed it to be a lipoma. The main discrepancy in the explanations generated was based on the typical features of lipoma and ganglionic cysts. While ChatGPT 3.5 has considered the location of joints to be the most common site for ganglionic cysts, ChatGPT 4.0 has given the diagnosis based on size and skin over the bump""",Ten clinical case scenarios pertaining to different dermatological conditions from a publicly available website:  https://www.clinicaladvisor.com/home/dermatology-clinic/.,"Manoharan P, Surapaneni KM. Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology. Indian J Dermatol Venereol Leprol. 2024 May 25:1-3. doi: 10.25259/IJDVL_1267_2023. Epub ahead of print. PMID: 38841923.",Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,18
1197,English,Clinical-based scenario,Poroma,PASS,1,ChatGPT 4,ChatGPT-4,3/14/2023,"""ChatGPT 3.5 has generated the wrong answer for the case scenario on Firm Round Papule on Foot. The answer generated by ChatGPT was ‘Verruca vulgaris’ whereas the answer was ‘Poroma’ in the key. But ChatGPT 4.0 has rightly diagnosed this case. In the case of a ‘swelling on the knee’, ChatGPT 3.5 rightly diagnosed the condition as a ganglionic cyst, whereas ChatGPT 4.0 diagnosed it to be a lipoma. The main discrepancy in the explanations generated was based on the typical features of lipoma and ganglionic cysts. While ChatGPT 3.5 has considered the location of joints to be the most common site for ganglionic cysts, ChatGPT 4.0 has given the diagnosis based on size and skin over the bump""",Ten clinical case scenarios pertaining to different dermatological conditions from a publicly available website:  https://www.clinicaladvisor.com/home/dermatology-clinic/.,"Manoharan P, Surapaneni KM. Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology. Indian J Dermatol Venereol Leprol. 2024 May 25:1-3. doi: 10.25259/IJDVL_1267_2023. Epub ahead of print. PMID: 38841923.",Assessing the diagnostic capability of ChatGPT through clinical case scenarios in dermatology.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,18
1198,English,"Malignancy discrimination, dermoscopic images",Melanoma,44.00%,221,ChatGPT 4V,ChatGPT-4V,12/17/2023,"In malignancy discrimination, Claude 3 Opus outperformed ChatGPT with 47.06% sensitivity, 81.63% specificity, and 64% accuracy, compared to 45.1%, 42.86%, and 44%, respectively. The McNemar test showed a significant difference (P<.001). Claude 3 Opus had an odds ratio of 3.951 (95% CI 1.685-9.263) in discriminating malignancy, while ChatGPT-4 had an odds ratio of 0.616 (95% CI 0.297-1.278).","100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) randomly selected from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. ","Liu X, Duan C, Kim MK, Zhang L, Jee E, Maharjan B, Huang Y, Du D, Jiang X. Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis. JMIR Med Inform. 2024 Aug 6;12:e59273. doi: 10.2196/59273. PMID: 39106482; PMCID: PMC11336503.",Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,22
1199,English,"Primary diagnosis, dermoscopic images",Melanoma,48.00%,221,ChatGPT 4V,ChatGPT-4V,12/17/2023,"In the primary diagnosis, Claude 3 Opus achieved 54.9% sensitivity (95% CI 44.08%-65.37%), 57.14% specificity (95% CI 46.31%-67.46%), and 56% accuracy (95% CI 46.22%-65.42%), while ChatGPT demonstrated 56.86% sensitivity (95% CI 45.99%-67.21%), 38.78% specificity (95% CI 28.77%-49.59%), and 48% accuracy (95% CI 38.37%-57.75%). The McNemar test showed no significant difference between the 2 models (P=.17). ","100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) randomly selected from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. ","Liu X, Duan C, Kim MK, Zhang L, Jee E, Maharjan B, Huang Y, Du D, Jiang X. Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis. JMIR Med Inform. 2024 Aug 6;12:e59273. doi: 10.2196/59273. PMID: 39106482; PMCID: PMC11336503.",Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,22
1200,English,"Primary diagnosis, dermoscopic images",Melanoma,56.00%,100,Claude 3,Claude 3 Opus,12/17/2023,"In the primary diagnosis, Claude 3 Opus achieved 54.9% sensitivity (95% CI 44.08%-65.37%), 57.14% specificity (95% CI 46.31%-67.46%), and 56% accuracy (95% CI 46.22%-65.42%), while ChatGPT demonstrated 56.86% sensitivity (95% CI 45.99%-67.21%), 38.78% specificity (95% CI 28.77%-49.59%), and 48% accuracy (95% CI 38.37%-57.75%). The McNemar test showed no significant difference between the 2 models (P=.17). ","100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) randomly selected from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. ","Liu X, Duan C, Kim MK, Zhang L, Jee E, Maharjan B, Huang Y, Du D, Jiang X. Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis. JMIR Med Inform. 2024 Aug 6;12:e59273. doi: 10.2196/59273. PMID: 39106482; PMCID: PMC11336503.",Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2024,Claude series,22
1201,English,"Malignancy discrimination, dermoscopic images",Melanoma,64.00%,100,Claude 3,Claude 3 Opus,12/17/2023,"In malignancy discrimination, Claude 3 Opus outperformed ChatGPT with 47.06% sensitivity, 81.63% specificity, and 64% accuracy, compared to 45.1%, 42.86%, and 44%, respectively. The McNemar test showed a significant difference (P<.001). Claude 3 Opus had an odds ratio of 3.951 (95% CI 1.685-9.263) in discriminating malignancy, while ChatGPT-4 had an odds ratio of 0.616 (95% CI 0.297-1.278).","100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) randomly selected from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. ","Liu X, Duan C, Kim MK, Zhang L, Jee E, Maharjan B, Huang Y, Du D, Jiang X. Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis. JMIR Med Inform. 2024 Aug 6;12:e59273. doi: 10.2196/59273. PMID: 39106482; PMCID: PMC11336503.",Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2024,Claude series,22
1202,English,"Differential diagnoses, dermoscopic images",Melanoma,76.00%,100,Claude 3,Claude 3 Opus,12/17/2023,"For the top 3 differential diagnoses, Claude 3 Opus and ChatGPT included the correct diagnosis in 76% (95% CI 66.33%-83.77%) and 78% (95% CI 68.46%-85.45%) of cases, respectively. ","100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) randomly selected from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. ","Liu X, Duan C, Kim MK, Zhang L, Jee E, Maharjan B, Huang Y, Du D, Jiang X. Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis. JMIR Med Inform. 2024 Aug 6;12:e59273. doi: 10.2196/59273. PMID: 39106482; PMCID: PMC11336503.",Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2024,Claude series,22
1203,English,"Differential diagnoses, dermoscopic images",Melanoma,78.00%,221,ChatGPT 4V,ChatGPT-4V,12/17/2023,"For the top 3 differential diagnoses, Claude 3 Opus and ChatGPT included the correct diagnosis in 76% (95% CI 66.33%-83.77%) and 78% (95% CI 68.46%-85.45%) of cases, respectively. ","100 histopathology-confirmed dermoscopic images (50 malignant, 50 benign) randomly selected from the International Skin Imaging Collaboration (ISIC) archive using a computer-generated randomization process. The ISIC archive was chosen due to its comprehensive and well-annotated collection of dermoscopic images, ensuring a diverse and representative sample. ","Liu X, Duan C, Kim MK, Zhang L, Jee E, Maharjan B, Huang Y, Du D, Jiang X. Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis. JMIR Med Inform. 2024 Aug 6;12:e59273. doi: 10.2196/59273. PMID: 39106482; PMCID: PMC11336503.",Claude 3 Opus and ChatGPT With GPT-4 in Dermoscopic Image Analysis for Melanoma Diagnosis: Comparative Performance Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,22
1204,Polish,Dermatology Practice Questions in Polish,Certification,70.00%,360,ChatGPT 4,ChatGPT-4,3/1/2023,,"360 questions in Polish and English: Three Specialty Certificate Examination in Dermatology tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice questions each (60% pass rate)","Lewandowski M, ?ukowicz P, ?wietlik D, Bara?ska-Rybak W. ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):686-691. doi: 10.1093/ced/llad255. PMID: 37540015.",ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,6
1205,English,Dermatology Practice Questions in English,Certification,80.00%,360,ChatGPT 4,ChatGPT-4,3/1/2023,,"360 questions in Polish and English: Three Specialty Certificate Examination in Dermatology tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice questions each (60% pass rate)","Lewandowski M, ?ukowicz P, ?wietlik D, Bara?ska-Rybak W. ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):686-691. doi: 10.1093/ced/llad255. PMID: 37540015.",ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,6
1206,Polish,"Clinical image-based Polish questions, Dermatology Practice","Images, Dermatology Certification",84.20%,360,ChatGPT 4,ChatGPT-4,3/1/2023,,"360 questions in Polish and English: Three Specialty Certificate Examination in Dermatology tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice questions each (60% pass rate)","Lewandowski M, ?ukowicz P, ?wietlik D, Bara?ska-Rybak W. ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):686-691. doi: 10.1093/ced/llad255. PMID: 37540015.",ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,6
1207,English,"Clinical image-based English questions, Dermatology Practice","Images, Dermatology Certification",93.00%,360,ChatGPT 4,ChatGPT-4,3/1/2023,,"360 questions in Polish and English: Three Specialty Certificate Examination in Dermatology tests, in English and Polish, consisting of 120 single-best-answer, multiple-choice questions each (60% pass rate)","Lewandowski M, ?ukowicz P, ?wietlik D, Bara?ska-Rybak W. ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology. Clin Exp Dermatol. 2024 Jun 25;49(7):686-691. doi: 10.1093/ced/llad255. PMID: 37540015.",ChatGPT-3.5 and ChatGPT-4 dermatological knowledge level based on the Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,6
1208,English,Actinic Keratosis: treatment,Actinic Keratosis,27.30%,11,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1209,English,Actinic Keratosis ,Actinic Keratosis,31.60%,38,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1210,English,Actinic Keratosis: diagnosis,Actinic Keratosis,37.50%,8,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1211,English,"Actinic Keratosis: Patient education, including pathogenesis of AK and potential risk factors",Actinic Keratosis,63.00%,19,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1212,English,Actinic Keratosis: What is the prevalence of actinic keratosis?,Actinic Keratosis,FAIL,1,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1213,English,Actinic Keratosis: What are the signs and symptoms of actinic keratosis?,Actinic Keratosis,FAIL,1,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1214,English,Actinic Keratosis: Which treatment for actinic keratosis gives the best cosmetic outcome?,Actinic Keratosis,FAIL,1,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,,38 clinical questions from professional dermatologists,"Lent HC, Ortner VK, Karmisholt KE, Wiegell SR, Nissen CV, Omland SH, Kamstrup MR, Togsverd?Bo K, Haedersdal M. A chat about actinic keratosis: examining capabilities and user experience of ChatGPT as a digital health technology in dermato?oncology. JEADV Clinical Practice. 2024 Mar;3(1):258-65. doi: 10.1002/jvc2.263",A chat about actinic keratosis: Examining capabilities and user experience of ChatGPT as a digital health technology in dermato�oncology,Dermatological Conditions and Management,Clinical Practice,2024,OpenAI GPT series,8
1215,English,Mohs Micrographic Surgery:  Sufficiency of Answers to Patient Questions for Clinical Practice,Mohs surgery,33.00%,150,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,About 92% of all responses were deemed appropriate for a patient-facing platform outside of the clinic by the majority of reviewers. The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th-grade reading level.,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1216,English,Answer's sufficiency for Clinical Practice - Will I need plastic surgery if I have Mohs surgery?,Mohs surgery,33.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1217,English,Answer's sufficiency for Clinical Practice - How deep do they cut for basal cell carcinoma?,Basal Cell Carcinoma,40.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1218,English,Appropriateness of the answer for Website - Will I need plastic surgery if I have Mohs surgery?,Mohs surgery,46.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1219,English,Answer's sufficiency for Clinical Practice - What are disadvantages of Mohs surgery?,Mohs surgery,46.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1220,English,Appropriateness of the answer for Website - How deep do they cut for basal cell carcinoma?,Basal Cell Carcinoma,53.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1221,English,Answer's sufficiency for Clinical Practice - Do you always need stitches after Mohs surgery?,Mohs surgery,53.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1222,English,Cosmetic dermatology: Will I need plastic surgery if I have Mohs surgery?,Mohs surgery,56.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1223,English,Sufficiency of Answers to Patient Questions for Clinical Practice,Mohs surgery,58.00%,150,Bard,Bard,3/21/2023,About 92% of all responses were deemed appropriate for a patient-facing platform outside of the clinic by the majority of reviewers. The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th-grade reading level.,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,Google's Family of LLMs,24
1224,English,Risks and Complications: What are disadvantages of Mohs surgery?,Mohs surgery,60.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1225,English,Appropriateness of the answer for Website - What are disadvantages of Mohs surgery?,Mohs surgery,60.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1226,English,Surgical Technique: How deep do they cut for basal cell carcinoma?,Basal Cell Carcinoma,64.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1227,English,Appropriateness of the answer for Website - What not to do after Mohs surgery?,Mohs surgery,66.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1228,English,Answer's sufficiency for Clinical Practice - What not to do after Mohs surgery?,Mohs surgery,66.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1229,English,Procedure Details: Do you always need stitches after Mohs surgery?,Mohs surgery,70.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1230,English,Appropriateness of the answer for Website - Do you always need stitches after Mohs surgery?,Mohs surgery,73.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1231,English,Answer's sufficiency for Clinical Practice - How is Mohs different from regular surgery?,Mohs surgery,73.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1232,English,Answer's sufficiency for Clinical Practice - How many hours does Mohs surgery take?,Mohs surgery,73.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1233,English,Mohs Micrographic Surgery Patient Questions: Accuracy,Mohs surgery,75.00%,150,ChatGPT 3.5,ChatGPT 3.5 Turbo,3/21/2023,About 92% of all responses were deemed appropriate for a patient-facing platform outside of the clinic by the majority of reviewers. The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th-grade reading level.,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1234,English,Post-operative Care: What not to do after Mohs surgery?,Mohs surgery,76.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1235,English,Recovery: How long is recovery from Mohs surgery?,Mohs surgery,80.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1236,English,Appropriateness of the answer for Website - How is Mohs different from regular surgery?,Mohs surgery,80.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1237,English,Appropriateness of the answer for Website - How many hours does Mohs surgery take?,Mohs surgery,80.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1238,English,Answer's sufficiency for Clinical Practice - How serious is Mohs surgery?,Mohs surgery,80.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1239,English,Answer's sufficiency for Clinical Practice - How painful is Mohs surgery?,Mohs surgery,80.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1240,English,Procedure Duration: How many hours does Mohs surgery take?,Mohs surgery,82.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1241,English,Comparison with Other Surgeries: How is Mohs different from regular surgery?,Mohs surgery,84.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1242,English,Pain and Discomfort: How painful is Mohs surgery?,Mohs surgery,86.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1243,English,Answer's sufficiency for Clinical Practice - Will I need anesthesia if I have Mohs surgery?,Mohs surgery,86.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1244,English,Appropriateness of the answer for Website - How serious is Mohs surgery?,Mohs surgery,86.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1245,English,Appropriateness of the answer for Website - How painful is Mohs surgery?,Mohs surgery,86.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1246,English,Answer's sufficiency for Clinical Practice - How long is recovery from Mohs surgery?,Mohs surgery,86.67%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1247,English,Risks and Complications: How serious is Mohs surgery?,Mohs surgery,90.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1248,English,Procedure details: Will I need anesthesia if I have Mohs surgery?,Mohs surgery,92.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1249,English,Mohs Micrographic Surgery Patient Questions: Appropriateness for Patient-facing web platform,Mohs surgery,92.00%,150,ChatGPT 3.5,ChatGPT 3.5 Turbo,3/21/2023,About 92% of all responses were deemed appropriate for a patient-facing platform outside of the clinic by the majority of reviewers. The majority of reviewers deemed all LLM responses appropriate. 75% of responses were rated as mostly accurate or higher. ChatGPT had the highest mean accuracy. The majority of the panel deemed 33% of responses sufficient for clinical practice. The mean comprehensibility scores for all platforms indicated a required 10th-grade reading level.,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1250,English,Appropriateness of the answer for Website - How long is recovery from Mohs surgery?,Mohs surgery,93.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1251,English,Appropriateness of the answer for Website - Will I need anesthesia if I have Mohs surgery?,Mohs surgery,93.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1252,English,Answer's sufficiency for Clinical Practice - What type of cancer is Mohs procedure for?,Mohs surgery,93.33%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1253,English,Applicability (Cancer Types): What type of cancer is Mohs procedure for?,Mohs surgery,100.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1254,English,Appropriateness of the answer for Website - What type of cancer is Mohs procedure for?,Mohs surgery,100.00%,15,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"10 most common patient MMS questions according to Google Search. Answers from ChatGPT and Bard were evaluated by 15 MMS surgeons, examining their appropriateness as a patient-facing informational platform, sufficiency of response in a clinical environment, and accuracy of content generated. Validated scales were employed to assess the comprehensibility of each response. Limitations include the significant degree of interrater variability observed.","Lauck KC, Cho SW, DaCunha M, Wuennenberg J, Aasi S, Alam M, Arron ST, Bar A, Brodland DG, Cerci FB, Cohen JL, Coldiron B, Council ML, Harmon CB, Hruza G, Läuchli S, Moody BR, Wysong AS, Zitelli JA, Tolkachjov SN. The utility of artificial intelligence platforms for patient-generated questions in Mohs micrographic surgery: a multi-national, blinded expert panel evaluation. Int J Dermatol. 2024 Nov;63(11):1592-1598. doi: 10.1111/ijd.17382. Epub 2024 Aug 9. PMID: 39123288.","The utility of artificial intelligence platforms for patientâ€�generated questions in Mohs micrographic surgery: a multiâ€�national, blinded expert panel evaluation",Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,24
1255,English,"Acne vulgaris, fifth-grade reading level (common condition)",Acne vulgaris,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1256,English,"Atopic dermatitis,  fifth-grade reading level (common condition)",Atopic dermatitis,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1257,English,"Bullous pemphigoid, fifth-grade reading level (rare condition)",Bullous pemphigoid,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1258,English,"Epidermolysis bullosa, fifth-grade reading level (rare condition)",Epidermolysis bullosa,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1259,English,"Epidermolysis bullosa, seventh-grade reading level (rare condition)",Epidermolysis bullosa,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1260,English,"Herpes zoster,  seventh-grade reading level (common condition)",Herpes zoster,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1261,English,"Herpes zoster, fifth-grade reading level (common condition)",Herpes zoster,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1262,English,"Lamellar ichthyosis, fifth-grade reading level (rare condition)",Lamellar ichthyosis,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1263,English,"Psoriasis,  fifth-grade reading level (common condition)",Psoriasis,0.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1264,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (common conditions),Patient Education,0.00%,40,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1265,English,"Bullous pemphigoid, fifth-grade reading level (rare condition)",Bullous pemphigoid,0.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1266,English,"Psoriasis,  fifth-grade reading level (common condition)",Psoriasis,0.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1267,English,"Atopic dermatitis,  seventh-grade reading level (common condition)",Atopic dermatitis,0.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1268,English,"Epidermolysis bullosa, seventh-grade reading level (rare condition)",Epidermolysis bullosa,0.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1269,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (rare conditions),Rare conditions,10.00%,40,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1270,English,"Atopic dermatitis,  fifth-grade reading level (common condition)",Atopic dermatitis,10.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1271,English,"Epidermolysis bullosa, fifth-grade reading level (rare condition)",Epidermolysis bullosa,10.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1272,English,"Epidermolysis bullosa, seventh-grade reading level (rare condition)",Epidermolysis bullosa,10.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1273,English,"Herpes zoster, fifth-grade reading level (common condition)",Herpes zoster,10.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1274,English,"Psoriasis,  seventh-grade reading level (common condition)",Psoriasis,10.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1275,English,"Bullous pemphigoid, seventh-grade reading level (rare condition)",Bullous pemphigoid,20.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1276,English,"Lichen planus, fifth-grade reading level (rare condition)",Lichen planus,20.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1277,English,"Bullous pemphigoid, seventh-grade reading level (rare condition)",Bullous pemphigoid,20.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1278,English,"Acne vulgaris, seventh-grade reading level (common condition)",Acne vulgaris,30.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1279,English,"Atopic dermatitis,  seventh-grade reading level (common condition)",Atopic dermatitis,30.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1280,English,"Epidermolysis bullosa, fifth-grade reading level (rare condition)",Epidermolysis bullosa,30.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1281,English,"Lichen planus, seventh-grade reading level (rare condition)",Lichen planus,30.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1282,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (rare conditions),Rare conditions,35.00%,40,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1283,English,"Lichen planus, fifth-grade reading level (rare condition)",Lichen planus,40.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1284,English,"Lamellar ichthyosis, fifth-grade reading level (rare condition)",Lamellar ichthyosis,40.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1285,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (common conditions),Common conditions,42.00%,40,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1286,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (common conditions),Common conditions,47.00%,40,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1287,English,"Bullous pemphigoid, seventh-grade reading level (rare condition)",Bullous pemphigoid,50.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1288,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (rare conditions),Rare conditions,50.00%,40,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1289,English,"Atopic dermatitis,  seventh-grade reading level (common condition)",Atopic dermatitis,50.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1290,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (rare conditions),Rare conditions,52.00%,40,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1291,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (rare conditions),Rare conditions,55.00%,40,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1292,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (common conditions),Common conditions,55.00%,40,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1293,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (common conditions),Common conditions,57.00%,40,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1294,English,"Psoriasis,  seventh-grade reading level (common condition)",Psoriasis,60.00%,10,Dermgpt,DermGPT,3/17/2023,Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEM,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1295,English,"Herpes zoster, fifth-grade reading level (common condition)",Herpes zoster,60.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1296,English,"Psoriasis,  seventh-grade reading level (common condition)",Psoriasis,60.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1297,English,"Acne vulgaris, fifth-grade reading level (common condition)",Acne vulgaris,60.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1298,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (common conditions),Common conditions,60.00%,40,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1299,English,"Psoriasis,  fifth-grade reading level (common condition)",Psoriasis,60.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1300,English,"Lamellar ichthyosis, seventh-grade reading level (rare condition)",Lamellar ichthyosis,70.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1301,English,"Atopic dermatitis,  fifth-grade reading level (common condition)",Atopic dermatitis,70.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1302,English,"Psoriasis,  seventh-grade reading level (common condition)",Psoriasis,70.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1303,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (rare conditions),Rare conditions,70.00%,40,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1304,English,"Herpes zoster,  seventh-grade reading level (common condition)",Herpes zoster,70.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1305,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (common conditions),Common conditions,72.00%,40,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1306,English,Dermatologic patient education material handouts meeting prompted seventh-grade reading level (rare conditions),Rare conditions,77.00%,40,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1307,English,"Lichen planus, fifth-grade reading level (rare condition)",Lichen planus,80.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1308,English,"Herpes zoster,  seventh-grade reading level (common condition)",Herpes zoster,80.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1309,English,"Lamellar ichthyosis, fifth-grade reading level (rare condition)",Lamellar ichthyosis,80.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1310,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (rare conditions),Rare conditions,82.00%,40,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1311,English,"Epidermolysis bullosa, fifth-grade reading level (rare condition)",Epidermolysis bullosa,90.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1312,English,"Lichen planus, seventh-grade reading level (rare condition)",Lichen planus,90.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1313,English,"Acne vulgaris, seventh-grade reading level (common condition)",Acne vulgaris,90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1314,English,"Bullous pemphigoid, fifth-grade reading level (rare condition)",Bullous pemphigoid,90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1315,English,"Bullous pemphigoid, seventh-grade reading level (rare condition)",Bullous pemphigoid,90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1316,English,"Lamellar ichthyosis, seventh-grade reading level (rare condition)",Lamellar ichthyosis,90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1317,English,"Lichen planus, seventh-grade reading level (rare condition)",Lichen planus,90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1318,English,"Psoriasis,  fifth-grade reading level (common condition)",Psoriasis,90.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1319,English,"Acne vulgaris, seventh-grade reading level (common condition)",Acne vulgaris,90.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1320,English,"Lamellar ichthyosis, seventh-grade reading level (rare condition)",Lamellar ichthyosis,90.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1321,English,Dermatologic patient education material handouts meeting prompted fifth-grade reading level (common conditions),Common conditions,90.00%,40,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1322,English,"Atopic dermatitis,  seventh-grade reading level (common condition)",Atopic dermatitis,100.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1323,English,"Lamellar ichthyosis, seventh-grade reading level (rare condition)",Lamellar ichthyosis,100.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1324,English,"Lichen planus, seventh-grade reading level (rare condition)",Lichen planus,100.00%,10,Dermgpt,DermGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1325,English,"Readability assessment, Comprehension level evaluation, Plain language check, Literacy appropriateness, Health literacy alignment: fifth-grade reading level",Acne vulgaris,100.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1326,English,"Readability assessment, Comprehension level evaluation, Plain language check, Literacy appropriateness, Health literacy alignment: seventh-grade reading level",Acne vulgaris,100.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1327,English,"Epidermolysis bullosa, seventh-grade reading level (rare condition)",Epidermolysis bullosa,100.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1328,English,"Herpes zoster,  seventh-grade reading level (common condition)",Herpes zoster,100.00%,10,Docsgpt,DocsGPT,3/17/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1329,English,"Acne vulgaris, fifth-grade reading level (common condition)",Acne vulgaris,100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1330,English,"Atopic dermatitis,  fifth-grade reading level (common condition)",Atopic dermatitis,100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1331,English,"Bullous pemphigoid, fifth-grade reading level (rare condition)",Bullous pemphigoid,100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1332,English,"Herpes zoster, fifth-grade reading level (common condition)",Herpes zoster,100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1333,English,"Lamellar ichthyosis, fifth-grade reading level (rare condition)",Lamellar ichthyosis,100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1334,English,"Lichen planus, fifth-grade reading level (rare condition)",Lichen planus,100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,,"960 PEMs were generated across 4 LLMs and 8 dermatologic conditions. Handouts were generated by ChatGPT-3.5, GPT-4, DocsGPT, and DermGPT at two prompted reading levels. The Flesch-Kincaid reading level (FKRL) of current American Academy of Dermatology PEMs was evaluated for 4 common (atopic dermatitis, acne vulgaris, psoriasis, and herpes zoster) and 4 rare (epidermolysis bullosa, bullous pemphigoid, lamellar ichthyosis, and lichen planus) dermatologic conditions. 10 PEMs per condition at unspecified fifth- and seventh-grade FKRLs were evaluated with Microsoft Word readability statistics. The preservation of meaning across LLMs was assessed by 2 blinded dermatology resident trainees.","Lambert R, Choo ZY, Gradwohl K, Schroedl L, Ruiz De Luzuriaga A. Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study. JMIR Dermatol. 2024 May 16;7:e55898. doi: 10.2196/55898. PMID: 38754096; PMCID: PMC11140271.",Assessing the Application of Large Language Models in Generating Dermatologic Patient Education Materials According to Reading Level: Qualitative Study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,13
1335,English,"Skin Melanoma, smoking status and mortality in patients' records",Melanoma,93.30%,661,Custom Ai,ULMFit,7/1/2021,"Specificity and Sensitivity for Never Smokers  (11,338 patients/600 with cutaneous melanoma), Former Smokers (5,789 patients/203), Persistent Smokers (5,904/62): 96%/96%, 98%/68%, 88%/99%","162000 sentences describing tobacco smoking behavior in records of 29823 patients (medical narrative comboined with ICD codes, histology, cancer treatment records and death certificated) diagnosed with cancer in 2009-2018 in Southwest Finland","Karlsson A, Ellonen A, Irjala H, Väliaho V, Mattila K, Nissi L, Kytö E, Kurki S, Ristamäki R, Vihinen P, Laitinen T, Ålgars A, Jyrkkiö S, Minn H, Heervä E. Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit. ESMO Open. 2021 Jun;6(3):100175. doi: 10.1016/j.esmoop.2021.100175. Epub 2021 Jun 3. PMID: 34091262; PMCID: PMC8182259.",Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit,Medical Records and Diagnostic Processes,Clinical Practice,2021,"NOT LLM, ULMFit",42
1336,English,"Skin Melanoma, smoking status and mortality in patients' records",Melanoma,93.50%,661,Bert,BERT,7/1/2021,"Specificity and Sensitivity for Never Smokers  (11,338 patients/600 with cutaneous melanoma), Former Smokers (5,789 patients/203), Persistent Smokers (5,904/62): 96%/96%, 98%/68%, 88%/99%","162000 sentences describing tobacco smoking behavior in records of 29823 patients (medical narrative comboined with ICD codes, histology, cancer treatment records and death certificated) diagnosed with cancer in 2009-2018 in Southwest Finland","Karlsson A, Ellonen A, Irjala H, Väliaho V, Mattila K, Nissi L, Kytö E, Kurki S, Ristamäki R, Vihinen P, Laitinen T, Ålgars A, Jyrkkiö S, Minn H, Heervä E. Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit. ESMO Open. 2021 Jun;6(3):100175. doi: 10.1016/j.esmoop.2021.100175. Epub 2021 Jun 3. PMID: 34091262; PMCID: PMC8182259.",Impact of deep learning-determined smoking status on mortality of cancer patients: never too late to quit,Medical Records and Diagnostic Processes,Clinical Practice,2021,"NOT general purpose LLM, Specialized LLM-Based Models: BERT-based classification models were trained to produce smoking phrase classifiers; Google's Family of LLMs",42
1337,English,Melanoma-specific questions,Melanoma,36.00%,50,Gemini 1.0,Gemini 1.0,3/17/2023,,50 melanoma-specific questions from Dutch dermatologists,"Kamminga NC, Kievits JE, Plaisier PW, Burgers JS, van der Veldt AM, van den Brand JAGJ, Mulder M, Wakkee M, Lugtenberg M, Nijsten T. Do Large Language Model Chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma. Br J Dermatol. 2024 Oct 4:ljae377. doi: 10.1093/bjd/ljae377. Epub ahead of print. PMID: 39365602.",Do Large Language Model Chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,40
1338,English,Melanoma-specific questions,Melanoma,44.00%,50,ChatGPT 4,ChatGPT-4,3/14/2023,,50 melanoma-specific questions from Dutch dermatologists,"Kamminga NC, Kievits JE, Plaisier PW, Burgers JS, van der Veldt AM, van den Brand JAGJ, Mulder M, Wakkee M, Lugtenberg M, Nijsten T. Do Large Language Model Chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma. Br J Dermatol. 2024 Oct 4:ljae377. doi: 10.1093/bjd/ljae377. Epub ahead of print. PMID: 39365602.",Do Large Language Model Chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,40
1339,English,Melanoma-specific questions,Melanoma,46.00%,50,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,50 melanoma-specific questions from Dutch dermatologists,"Kamminga NC, Kievits JE, Plaisier PW, Burgers JS, van der Veldt AM, van den Brand JAGJ, Mulder M, Wakkee M, Lugtenberg M, Nijsten T. Do Large Language Model Chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma. Br J Dermatol. 2024 Oct 4:ljae377. doi: 10.1093/bjd/ljae377. Epub ahead of print. PMID: 39365602.",Do Large Language Model Chatbots perform better than established patient information resources in answering patient questions? A comparative study on melanoma,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,40
1340,English,"All Psoriasis affected body areas, including nails and joints, recognized in any given patient's case, from EMR records",Psoriasis,57.40%,94,ChatGPT 4V,ChatGPT-4V,3/17/2023,"From 94 cases, complete accuracy was achieved in 54 cases (57.4%), although inaccuracies were observed in 40 cases (42.6%). ","477 psoriasis-affected body areas inferred from EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4’s analysis was compared with that of a senior dermatologist.","Jonathan Shapiro, Sharon Baum, Felix Pavlotzky, Yaron Ben Mordehai, Aviv Barzilai, Tamar Freud, Rotem Gershon, Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data, Clinics in Dermatology, Volume 42, Issue 5, 2024, Pages 480-486, ISSN 0738-081X, https://doi.org/10.1016/j.clindermatol.2024.06.018. https://www.sciencedirect.com/science/article/pii/S0738081X24001020",Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data,Dermatological Conditions and Management,Professional Education,2024,OpenAI GPT series,20
1341,English,Nail involvement in psoriasis,Psoriasis,90.63%,94,ChatGPT 4V,ChatGPT-4V,3/17/2023,"Nail involvement was detected in 32 cases (34.0% of all cases), with ChatGPT-4 correctly identifying 29 cases","477 psoriasis-affected body areas inferred from EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4’s analysis was compared with that of a senior dermatologist.","Jonathan Shapiro, Sharon Baum, Felix Pavlotzky, Yaron Ben Mordehai, Aviv Barzilai, Tamar Freud, Rotem Gershon, Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data, Clinics in Dermatology, Volume 42, Issue 5, 2024, Pages 480-486, ISSN 0738-081X, https://doi.org/10.1016/j.clindermatol.2024.06.018. https://www.sciencedirect.com/science/article/pii/S0738081X24001020",Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data,Dermatological Conditions and Management,Professional Education,2024,OpenAI GPT series,20
1342,English,"Psoriasis affected body areas, including nails and joints, recognized from EMR records",Psoriasis,92.80%,477,ChatGPT 4V,ChatGPT-4V,3/17/2023,"While senior dermatologist identified 477 psoriasis-affected body areas, ChatGPT-4 accurately recognized 443 (92.8%) of these areas, missed 34, and incorrectly identified 30 areas as affected.","477 psoriasis-affected body areas inferred from EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4’s analysis was compared with that of a senior dermatologist.","Jonathan Shapiro, Sharon Baum, Felix Pavlotzky, Yaron Ben Mordehai, Aviv Barzilai, Tamar Freud, Rotem Gershon, Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data, Clinics in Dermatology, Volume 42, Issue 5, 2024, Pages 480-486, ISSN 0738-081X, https://doi.org/10.1016/j.clindermatol.2024.06.018. https://www.sciencedirect.com/science/article/pii/S0738081X24001020",Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data,Dermatological Conditions and Management,Professional Education,2024,OpenAI GPT series,20
1343,English,Joint involvement in psoriasis,Psoriasis,96.00%,25,ChatGPT 4V,ChatGPT-4V,3/17/2023,"Joint involvement was noted in 25 cases (26.6% of all cases), with 24 correctly identified using ChatGPT-4.","477 psoriasis-affected body areas inferred from EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4’s analysis was compared with that of a senior dermatologist.","Jonathan Shapiro, Sharon Baum, Felix Pavlotzky, Yaron Ben Mordehai, Aviv Barzilai, Tamar Freud, Rotem Gershon, Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data, Clinics in Dermatology, Volume 42, Issue 5, 2024, Pages 480-486, ISSN 0738-081X, https://doi.org/10.1016/j.clindermatol.2024.06.018. https://www.sciencedirect.com/science/article/pii/S0738081X24001020",Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients’ data,Dermatological Conditions and Management,Professional Education,2024,OpenAI GPT series,20
1344,English,Benign and Malignant Neoplasms,Mohs surgery,30.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,Practice dermatology board certification examination.,"Joly-Chevrier M, Nguyen AX, Lesko-Krleza M, Lefrançois P. Performance of ChatGPT on a practice dermatology board certification examination. Journal of cutaneous medicine and surgery. 2023 Jul;27(4):407-9.",Performance of ChatGPT on a practice dermatology board certification examination,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,5
1345,English,Dermatology Practice Exam,Certification,59.30%,241,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,No Association Between Question Length and Accuracy. Wilcoxon Rank-Sum (Mann-Whitney) Test p value = 0.13,Practice dermatology board certification examination.,"Joly-Chevrier M, Nguyen AX, Lesko-Krleza M, Lefrançois P. Performance of ChatGPT on a practice dermatology board certification examination. Journal of cutaneous medicine and surgery. 2023 Jul;27(4):407-9.",Performance of ChatGPT on a practice dermatology board certification examination,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,5
1346,English,Pediatric Dermatology,Mohs surgery,80.00%,5,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,Practice dermatology board certification examination.,"Joly-Chevrier M, Nguyen AX, Lesko-Krleza M, Lefrançois P. Performance of ChatGPT on a practice dermatology board certification examination. Journal of cutaneous medicine and surgery. 2023 Jul;27(4):407-9.",Performance of ChatGPT on a practice dermatology board certification examination,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,5
1347,English,Basic Science and Structure of the Skin,Mohs surgery,80.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,Practice dermatology board certification examination.,"Joly-Chevrier M, Nguyen AX, Lesko-Krleza M, Lefrançois P. Performance of ChatGPT on a practice dermatology board certification examination. Journal of cutaneous medicine and surgery. 2023 Jul;27(4):407-9.",Performance of ChatGPT on a practice dermatology board certification examination,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,5
1348,English,"Erythema, vascular disorders, urticaria (topics tested in Dermatology certificate), English",Erythema,27.30%,11,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1349,Korean,"Pigmentation, metabolic, genetic, and endocrine disorders (topics tested in Dermatology certificate), Korean","Pigmentary disorders, Genetics, Endocrinology, Metabolic disorders",33.30%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1350,Korean,"Papulosquamous disorders (topics tested in Dermatology certificate), Korean",Papulosquamous disorders,42.10%,19,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1351,Korean,"Symptom, Diagnosis (Evaluation functions tested in Dermatology certificate), Korean",Diagnostics,42.30%,59,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1352,Korean,"Judgement (Cognitive functions tested in Dermatology certificate), Korean",Judgement,46.30%,54,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1353,English,"Pigmentation, metabolic, genetic, and endocrine disorders (topics tested in Dermatology certificate), English","Pigmentary disorders, Genetics, Endocrinology, Metabolic disorders",50.00%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1354,Korean,"Eczematous disorders (topics tested in Dermatology certificate), Korean",Eczema,50.00%,22,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1355,Korean,"Problem Solving (Cognitive functions tested in Dermatology certificate), Korean",Problem Solving,51.60%,64,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1356,Korean,"Erythema, vascular disorders, urticaria (topics tested in Dermatology certificate), Korean","Erythema, vascular disorders, urticaria",54.50%,11,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1357,English,"Autoimmune and vesiculobullous disorders (topics tested in Dermatology certificate), English","Autoimmune Disorders, Vesiculobullous disorders",55.60%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1358,Korean,"Disorders of skin Appendages and mucous membranes (topics tested in Dermatology certificate), Korean","Disorders of skin, Certification",55.60%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1359,Korean,"Benign and malignant neoplasms (topics tested in Dermatology certificate), Korean",Benign and malignant neoplasms,55.60%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1360,English,"Physical stimuli, fat atrophy, cosmetic dermatology, and others (topics tested in Dermatology certificate), English",Cosmetic Dermatology,57.00%,29,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1361,Korean,Dermatology Specialty Certificate Examination (Korean),Certification,57.00%,200,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1362,Korean,"Treatment, Prognosis (Evaluation functions tested in Dermatology certificate), Korean",Therapy,60.00%,70,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1363,Korean,"Autoimmune and vesiculobullous disorders (topics tested in Dermatology certificate), Korean","Autoimmune Disorders, Vesiculobullous disorders",61.10%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1364,Korean,"Infectious disorders (topics tested in Dermatology certificate), Korean",Infectious disorders,62.10%,29,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1365,English,"Symptom, Diagnosis (Evaluation functions tested in Dermatology certificate), English",Diagnostics,62.70%,59,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1366,English,"Papulosquamous disorders (topics tested in Dermatology certificate), English",Papulosquamous,63.20%,19,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1367,English,"Problem Solving (Cognitive functions tested in Dermatology certificate), English",Problem Solving,64.00%,64,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1368,English,"Judgement (Cognitive functions tested in Dermatology certificate), English",Judgement,64.80%,54,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1369,Korean,"Definition, Cause, Mechanism (Evaluation functions tested in Dermatology certificate), Korean",Diagnostic Foundations,66.20%,71,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1370,Korean,"Memorization (Cognitive functions tested in Dermatology certificate), Korean",Memorization,68.30%,82,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1371,Korean,"Physical stimuli, fat atrophy, cosmetic dermatology, and others (topics tested in Dermatology certificate), Korean",Cosmetic Dermatology,69.00%,29,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1372,English,Dermatology Specialty Certificate Examination (English),Certification,69.00%,200,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1373,English,"Treatment, Prognosis (Evaluation functions tested in Dermatology certificate), English",Therapy,70.00%,70,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1374,English,"Eczematous disorders (topics tested in Dermatology certificate), English",Eczema,72.70%,22,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1375,English,"Definition, Cause, Mechanism (Evaluation functions tested in Dermatology certificate), English",Diagnostic Foundations,73.20%,71,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1376,English,"Memorization (Cognitive functions tested in Dermatology certificate), English",Memorization,75.60%,82,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1377,Korean,"Basic Dermatology (topics tested in Dermatology certificate), Korean",Basic dermatology,77.80%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1378,English,"Infectious disorders (topics tested in Dermatology certificate), English",Infectious disorders,82.80%,29,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1379,English,"Basic Dermatology (topics tested in Dermatology certificate), English",Basic dermatology,83.30%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1380,English,"Benign and malignant neoplasms (topics tested in Dermatology certificate), English",Benign and malignant neoplasms,83.30%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1381,English,"Disorders of skin Appendages and mucous membranes (topics tested in Dermatology certificate), English","Disorders of skin, Certification",88.90%,18,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,"200 text-based questions designed based on the test blueprint of Dermatology SCE in Korea. In Korea the dermatology specialty certification examination (SCE) comprises 200 best-of-five multiple-choice questions (MCQs), half of which consists of clinical photographs and histological images and another half consists of text-based MCQs.","Joh HC, Kim MH, Ko JY, Kim JS, Jue MS. Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings. Indian J Dermatol. 2024 Jul-Aug;69(4):338-341. doi: 10.4103/ijd.ijd_1050_23. Epub 2024 Aug 19. PMID: 39296696; PMCID: PMC11407566.",Evaluating the Performance of ChatGPT in Dermatology Specialty Certificate Examination-style Questions: A Comparative Analysis between English and Korean Language Settings,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,26
1382,English,"Image-based FST V-VI, top-1 diagnosis",Fitzpatrick V-VI skin type,4.35%,207,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1383,English,"Image-based FST I-II, top-1 diagnosis",Fitzpatrick I-II skin type,5.28%,208,ChatGPT 4V,ChatGPT-4V,11/17/2023,"For Fitzpatrick skin tone (FST) prediction, GPT-4V only provided skin tones for 603 images and reported there was not enough information for the remaining 53 images. Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. ","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1384,English,Image-based diagnostics of dermatology conditions: Top diagnosis,"Images, Diagnostics",6.20%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,"In dermatology, GPT-4V had an overall top-1 and top-3 diagnostic accuracy of 6.2% and 21% respectively.. There was a significant accuracy drop when predicting on images of darker skin tones (p<0.001). GPT-4V accurately identified Fitzpatrick skin tones for 56.5% of images. For the multiple-choice styled USMLE image-based test questions, GPT-4V had an accuracy of 59%. ","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1385,English,"Image-based FST III-IV, top-1 diagnosis","Images, Diagnostics",8.71%,241,ChatGPT 4V,ChatGPT-4V,11/17/2023,"For Fitzpatrick skin tone (FST) prediction, GPT-4V only provided skin tones for 603 images and reported there was not enough information for the remaining 53 images. Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. ","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1386,English,Image-based FST V-VI top-3 diagnosis,"Images, Diagnostics",15.94%,207,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1387,English,"Image-based FST I-II, top-3 diagnosis","Images, Diagnostics",16.82%,208,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1388,English,Image-based diagnostics of dermatology conditions: Differential diagnosis (top 3),Differential diagnosis,21.00%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1389,English,"Image-based FST III-IV, top-3 diagnosis","Images, Diagnostics",29.46%,241,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1390,English,Melanoma in situ; Image-based diagnostics,Melanoma,37.80%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,"GPT-4V predicts melanoma in situ at a frequency of 37.8% compared to the true frequency of 0.91%. This overdiagnosis could imply that the model is overly sensitive to features associated with melanoma in situ, leading it to misclassify other conditions as this type of melanoma.","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1391,English,Melanocytic nevi; Image-based diagnostics,Moles,39.00%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,Top predictions for GPT-4V are mostly malignant dermatological conditions,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1392,English,Malignancy predictions from dermatological images: accuracy,"Images, Diagnostics",39.62%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,"In malignancy predictions, Dermatologists had an accuracy of 67.99% (95% CI: 64.27% - 71.55%) compared to GPT-4V’s accuracy of 39.62% (95% CI: 35.41% - 43.95%). This difference was statistically significant with a p-value of 3.93×10?21. However, GPT-4V outperformed in other metrics including a sensitivity of 0.86 compared to dermatologists’ 0.71","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1393,English,Image-based diagnostics of dermatology conditions: Fitzpatrick skin tone (FST) labeling,"Images, Diagnostics",56.50%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,"For Fitzpatrick skin tone (FST) prediction, GPT-4V only provided skin tones for 603 images and reported there was not enough information for the remaining 53 images. Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. ","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1394,English,Image-based diagnostics of dermatology conditions: USMLE questions,"Images, Diagnostics",59.00%,612,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1395,English,Squamous cell carcinoma; Image-based diagnostics,Squamous Cell Carcinoma,59.00%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1396,English,Squamous cell carcinoma in-situ (SCCIS); Image-based diagnostics,Squamous Cell Carcinoma,68.00%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1397,English,Basal cell carcinoma; Image-based diagnostics,Basal Cell Carcinoma,83.00%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,,"Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1398,English,Malignancy predictions from dermatological images: sensitivity,"Images, Diagnostics",86.00%,656,ChatGPT 4V,ChatGPT-4V,11/17/2023,"When comparing dermatologists to GPT-4V’s malignancy predictions,  dermatologists had an accuracy of 67.99% (95% CI: 64.27% - 71.55%) compared to GPT-4V’s accuracy of 39.62% (95% CI: 35.41% - 43.95%). This difference was statistically significant with a p-value of 3.93×10?21. However, GPT-4V outperformed in other metrics including a sensitivity of 0.86 compared to dermatologists’ 0.71.","Diverse Dermatology Images (DDI) Dataset representative of diverse skin tones. DDI contains 656 clinical images obtained from the Stanford Clinic and includes some rare dermatological conditions that have previously been described in literature. The Fitzpatrick skin tone (FST) was carefully labeled based on in-person visit documentation, demographic photo, and image of the lesion. They are represented as groups of two i.e. FST I-II (light skin tone), FST III-IV, and FST V-VI (dark skin tone). There are 208, 241, and 207 clinical images across the three groups respectively. USMLE set included 612 questions","Jiang Y, Omiye JA, Zakka C, Moor M, Gui H, Alipour S, Mousavi SS, Chen JH, Rajpurkar P, Daneshjou R. Evaluating General Vision-Language Models for Clinical Medicine. medRxiv. 2024 Apr 14:2024-04. doi: https://doi.org/10.1101/2024.04.12.24305744",Evaluating General Vision-Language Models for Clinical Medicine,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,32
1399,English,"Medication Recommendations for Common Dermatological Conditions, Q-value cutoff of 10 (least confident disease-medication associations)",Dermatology medications,72.92%,N/A,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"Drug suggestions were also evaluated using the Q-value, measure used to assess the strength or reliability of disease-medication associations in the drug suggestion model. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. Human expert validation agreement surpassed Q-value cutoff-based agreement. ","survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. ","Iqbal U, Lee LT, Rahmanti AR, Celi LA, Li YJ. Can large language models provide secondary reliable opinion on treatment options for dermatological diseases? J Am Med Inform Assoc. 2024 May 20;31(6):1341-1347. doi: 10.1093/jamia/ocae067. PMID: 38578616; PMCID: PMC11105123.",Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?,Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,14
1400,English,"Medication Recommendations for Common Dermatological Conditions, Q-value cutoff of 5 (moderate level confidence in disease-medication associations)",Dermatology medications,85.42%,N/A,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"Drug suggestions were also evaluated using the Q-value, measure used to assess the strength or reliability of disease-medication associations in the drug suggestion model. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. Human expert validation agreement surpassed Q-value cutoff-based agreement. ","survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. ","Iqbal U, Lee LT, Rahmanti AR, Celi LA, Li YJ. Can large language models provide secondary reliable opinion on treatment options for dermatological diseases? J Am Med Inform Assoc. 2024 May 20;31(6):1341-1347. doi: 10.1093/jamia/ocae067. PMID: 38578616; PMCID: PMC11105123.",Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?,Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,14
1401,English,"Medication Recommendations for Common Dermatological Conditions, Q-value cutoff of 3 (most confident disease-medication associations)",Dermatology medications,95.14%,N/A,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"Drug suggestions were also evaluated using the Q-value, measure used to assess the strength or reliability of disease-medication associations in the drug suggestion model. Varying cutoff values for disease-medication associations, a cutoff of 3 achieved 95.14% accurate prescriptions, 5 yielded 85.42%, and 10 resulted in 72.92%. Human expert validation agreement surpassed Q-value cutoff-based agreement. ","survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. ","Iqbal U, Lee LT, Rahmanti AR, Celi LA, Li YJ. Can large language models provide secondary reliable opinion on treatment options for dermatological diseases? J Am Med Inform Assoc. 2024 May 20;31(6):1341-1347. doi: 10.1093/jamia/ocae067. PMID: 38578616; PMCID: PMC11105123.",Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?,Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,14
1402,English,Medication Recommendations for Common Dermatological Conditions,Dermatology medications,98.87%,N/A,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"ChatGPT achieved a high 98.87% dermatologist approval rate for common dermatological medication recommendations. . While ChatGPT offered accurate drug advice, it occasionally included incorrect ATC codes, leading to issues like incorrect drug use and type, nonexistent codes, repeated errors, and incomplete medication codes. ","survey questions in April 2023 for drug recommendations generated by ChatGPT with data from secondary databases, that is, Taiwan's National Health Insurance Research Database and an US medical center database, and validated by dermatologists. ","Iqbal U, Lee LT, Rahmanti AR, Celi LA, Li YJ. Can large language models provide secondary reliable opinion on treatment options for dermatological diseases? J Am Med Inform Assoc. 2024 May 20;31(6):1341-1347. doi: 10.1093/jamia/ocae067. PMID: 38578616; PMCID: PMC11105123.",Can large language models provide secondary reliable opinion on treatment options for dermatological diseases?,Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,14
1403,English,"Surgery, Pediatric Dermatology",Mohs surgery,40.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1404,English,"Surgery, Pediatric Dermatology",Mohs surgery,60.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1405,English,Pediatric Dermatology (multiple choice and mutiple answer questions),Mohs surgery,76.20%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1406,English,"Pharmacology, Pediatric Dermatology","Pharmacology, Pediatric Dermatology",80.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1407,English,Pediatric Dermatology (multiple choice and mutiple answer questions),Pediatric Dermatology,90.50%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1408,English,"Basic Sciences, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1409,English,"Research Methods, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1410,English,"Genetics, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1411,English,"Connetvie Tissue Diseases, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1412,English,"Dermopathology, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1413,English,"Pigmentary Lesions, Pediatric Dermatology",Pigmentary disorders,100.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1414,English,"Basic Sciences, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1415,English,"Research Methods, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1416,English,"Genetics, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1417,English,"Connetvie Tissue Diseases, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1418,English,"Pharmacology, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1419,English,"Dermopathology, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1420,English,"Pigmentary Lesions, Pediatric Dermatology",Pigmentary disorders,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1421,English,"Medical Billing, Pediatric Dermatology",Pediatric dermatology,100.00%,21,ChatGPT 4,ChatGPT-4,3/14/2023,,"24 text-based questions completed by five pediatric dermatologists. These included 16 single-answer, multiple-choice questions  (MCQs), two multiple-answer questions (MAQs), and six free-response, case-based questions (CBQs). MCQs and MAQs were selected from the American Board of Dermatology (ABD) 2021 Certification Sample Test and used with permission from the ABD.7 CBQs were extracted from the “Photoquiz” section of the journal Pediatric Dermatology from issues published between July 2022 and July 2023, which lie beyond ChatGPT-3.5 and 4.0's training period.","Huang CY, Zhang E, Caussade MC, Brown T, Stockton Hogrogian G, Yan AC. Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT. Pediatr Dermatol. 2024 Sep-Oct;41(5):831-834. doi: 10.1111/pde.15649. Epub 2024 May 9. PMID: 38721744.",Pediatric dermatologists versus AI bots: Evaluating the medical knowledge and diagnostic capabilities of ChatGPT,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,16
1422,English,Practicing Dermatologists who find LLMs extremely accurate ,LLMs,7.00%,86,ChatGPT 3.5,"ChatGPT-3.5, ChatGPT-4, Bard, Bing Chat, BioGPT",11/30/2022,,"134 responses to 18 questions about LLMs. Of 134 respondents, 87 respondents (64.9%) have used LLMs, with 45 respondents (51.7%) using the technology daily or weekly and 77 respondents (88.5%) reporting feeling extremely likely or somewhat likely to continue to use LLMs in the future. ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). 107 were post-residency level dermatology clinicials and 27 were current residents.","Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol. 2024 Oct;144(10):2298-2301. doi: 10.1016/j.jid.2024.03.028. Epub 2024 Apr 4. PMID: 38582369.",Dermatologists’ Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,35
1423,English,Practicing Dermatologists requiring minimal editing (<5 changes) to LLM responses,LLMs,41.40%,87,ChatGPT 3.5,"ChatGPT-3.5, ChatGPT-4, Bard, Bing Chat, BioGPT",11/30/2022,,"134 responses to 18 questions about LLMs. Of 134 respondents, 87 respondents (64.9%) have used LLMs, with 45 respondents (51.7%) using the technology daily or weekly and 77 respondents (88.5%) reporting feeling extremely likely or somewhat likely to continue to use LLMs in the future. ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). 107 were post-residency level dermatology clinicials and 27 were current residents.","Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol. 2024 Oct;144(10):2298-2301. doi: 10.1016/j.jid.2024.03.028. Epub 2024 Apr 4. PMID: 38582369.",Dermatologists’ Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,35
1424,English,Practicing Dermatologists extremely likely to continue using LLMs,LLMs,51.70%,87,ChatGPT 3.5,"ChatGPT-3.5, ChatGPT-4, Bard, Bing Chat, BioGPT",11/30/2022,,"134 responses to 18 questions about LLMs. Of 134 respondents, 87 respondents (64.9%) have used LLMs, with 45 respondents (51.7%) using the technology daily or weekly and 77 respondents (88.5%) reporting feeling extremely likely or somewhat likely to continue to use LLMs in the future. ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). 107 were post-residency level dermatology clinicials and 27 were current residents.","Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol. 2024 Oct;144(10):2298-2301. doi: 10.1016/j.jid.2024.03.028. Epub 2024 Apr 4. PMID: 38582369.",Dermatologists’ Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,35
1425,English,Practicing Dermatologists who find LLMs somewhat accurate,LLMs,58.10%,86,ChatGPT 3.5,"ChatGPT-3.5, ChatGPT-4, Bard, Bing Chat, BioGPT",11/30/2022,,"134 responses to 18 questions about LLMs. Of 134 respondents, 87 respondents (64.9%) have used LLMs, with 45 respondents (51.7%) using the technology daily or weekly and 77 respondents (88.5%) reporting feeling extremely likely or somewhat likely to continue to use LLMs in the future. ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). 107 were post-residency level dermatology clinicials and 27 were current residents.","Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol. 2024 Oct;144(10):2298-2301. doi: 10.1016/j.jid.2024.03.028. Epub 2024 Apr 4. PMID: 38582369.",Dermatologists’ Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,35
1426,English,Practicing Dermatologists concerned about accuracy of LLMs,LLMs,78.00%,132,ChatGPT 3.5,"ChatGPT-3.5, ChatGPT-4, Bard, Bing Chat, BioGPT",11/30/2022,,"134 responses to 18 questions about LLMs. Of 134 respondents, 87 respondents (64.9%) have used LLMs, with 45 respondents (51.7%) using the technology daily or weekly and 77 respondents (88.5%) reporting feeling extremely likely or somewhat likely to continue to use LLMs in the future. ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). 107 were post-residency level dermatology clinicials and 27 were current residents.","Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol. 2024 Oct;144(10):2298-2301. doi: 10.1016/j.jid.2024.03.028. Epub 2024 Apr 4. PMID: 38582369.",Dermatologists’ Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,35
1427,English,ChatGPT's popularity among dermatologists using LLMs,ChatGPT,85.10%,87,ChatGPT 3.5,"ChatGPT-3.5, ChatGPT-4",11/30/2022,"ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). Most popular uses of LLM among respondents included asking questions about diagnoses and medical management, creating patient handouts, and writing insurance authorization letters.","134 responses to 18 questions about LLMs. Of 134 respondents, 87 respondents (64.9%) have used LLMs, with 45 respondents (51.7%) using the technology daily or weekly and 77 respondents (88.5%) reporting feeling extremely likely or somewhat likely to continue to use LLMs in the future. ChatGPT was the most popular LLM used (85.1%), followed by Google Bard (25.3%). 107 were post-residency level dermatology clinicials and 27 were current residents.","Gui H, Rezaei SJ, Schlessinger D, Weed J, Lester J, Wongvibulsin S, Mitchell D, Ko J, Rotemberg V, Lee I, Daneshjou R. Dermatologists' Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey. J Invest Dermatol. 2024 Oct;144(10):2298-2301. doi: 10.1016/j.jid.2024.03.028. Epub 2024 Apr 4. PMID: 38582369.",Dermatologists’ Perspectives and Usage of Large Language Models in Practice: An Exploratory Survey,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,35
1428,English,Top Diagnosis Matched,Atopic dermatitis,0.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 0% CC, 82% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1429,English,Top Diagnosis Matched,Merkel Cell Carcinoma,0.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 0% CC, 27% MC, 45% PC, 27% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1430,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Ecthyma,0.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 0% CC, 27% MC, 55% PC, 18% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1431,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,cutaneous T-cell lymphoma,0.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 0% CC, 64% MC, 18% PC, 18% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1432,English,Differential Diagnosis Completely Correct or Mostly Correct,Merkel Cell Carcinoma,27.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"0% CC, 27% MC, 45% PC, 27% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1433,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Ecthyma,27.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"0% CC, 27% MC, 55% PC, 18% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1434,English,Top Diagnosis/Diagnosis and Workup of Dermatologic Conditions,Diagnostics,37.50%,110,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1435,English,Per-Case Inclusion in diagnosis,Basal Cell Carcinoma,40.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1436,English,Per-Case Inclusion in diagnosis,Ecthyma,40.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1437,English,Per-Case Inclusion in diagnosis,Syphilis,40.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1438,English,Correct diagnosis (top Dx),Basal Cell Carcinoma,40.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1439,English,Correct diagnosis (top Dx),Ecthyma,40.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1440,English,Correct diagnosis (top Dx),Syphilis,40.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1441,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Tinea corporis,45.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"0% CC, 45% MC, 55% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1442,English,Per-Case Inclusion in diagnosis,cutaneous T-cell lymphoma,50.00%,4,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1443,English,Correct diagnosis (top Dx),cutaneous T-cell lymphoma,50.00%,4,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1444,English,Most likely diagnosis (differential diagnoses of  dermatologic conditions described from the perspective of a non-dermatologist physician),Diagnosis,50.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"The percentage of cases where ChatGPT-4's most likely diagnosis matched the top diagnosis on the dermatologists' list. This occurred in 50% of the vignettes: basal cell carcinoma, viral exanthem, syphilis, psoriasis, and melanoma","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1445,English,Overall Accuracy of individual diagnosis suggested by ChatGPT-4 across all cases.,Diagnostics,52.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Proxy for Accuracy: rate of accurate diagnoses and the percentage of matching diagnoses.  Overall, 52% of ChatGPT4’s diagnoses were accurate and 62% of its recommended workup suggestions were deemed completely correct by board-certified dermatologists.  The percentage of ChatGPT-4's diagnoses that were also found on the dermatologists' differential diagnosis lists: 52% across all vignettes. The percentage of correct diagnoses provided by ChatGPT-4 for each individual vignette ranged from 20% to 100%.","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1446,English,Per-Case Inclusion in diagnosis,Viral exanthem,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1447,English,Per-Case Inclusion in diagnosis,Merkel Cell Carcinoma,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1448,English,Per-Case Inclusion in diagnosis,Tinea corporis,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1449,English,Per-Case Inclusion in diagnosis,Melanoma,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1450,English,Correct diagnosis (top Dx),Viral exanthem,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1451,English,Correct diagnosis (top Dx),Merkel Cell Carcinoma,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1452,English,Correct diagnosis (top Dx),Tinea corporis,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1453,English,Correct diagnosis (top Dx),Melanoma,60.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1454,English,Workup suggested (diagnostic steps a physician would take to confirm a suspected diagnosis) deemed completely correct by dermatologists,Viral exanthem,62.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,ChatGPT-4 was rated as providing a completely correct workup an average of 62% of the time for any given clinical vignette ,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1455,English,Completely correct workup of dermatological conditions,Workup,62.00%,110,ChatGPT 4,ChatGPT-4,3/14/2023,"Out of 10 vignettes, ChatGPT-4 was rated as providing a completely correct, or mostly correct workup in all vignettes except for those describing atopic dermatitis, Merkel cell carcinoma, and ecthyma (rated by 11 dermatologists)","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1456,English,"CTCL, cutaneous T-cell lymphoma/Differential Diagnosis Completely Completely Correct or Mostly Correct",cutaneous T-cell lymphoma,64.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"0% CC, 64% MC, 18% PC, 18% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1457,English,"MCC, Merkel cell carcinoma - Differential Diagnosis Completely or Mostly Correct",Merkel Cell Carcinoma,73.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1458,English,Per-Case Inclusion/Differential Diagnosis (the correct diagnosis is listed somewhere in its differential diagnosis ),Differential diagnosis,80.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,Correct in Differential Diagnosis: 80%,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1459,English,Differential Diagnosis Completely Correct or Mostly Correct,Atopic dermatitis,82.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"0% CC, 82% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1460,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Psoriasis,82.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"0% CC, 82% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1461,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Syphilis,82.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"27% CC, 55% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1462,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Melanoma,82.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"27% CC, 55% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1463,English,Workup Completely or Mostly Correct,Atopic dermatitis,82.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1464,English,Workup Completely or Mostly Correct,Merkel Cell Carcinoma,82.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1465,English,Differential Diagnosis Completely or Mostly Correct,Ecthyma,82.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1466,English,Differential Diagnosis Completely or Mostly Correct,cutaneous T-cell lymphoma,82.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1467,English,Differential Diagnosis Completely Correct or Mostly Correct,Viral exanthem,91.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"18% CC, 73% MC, 9% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1468,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Basal Cell Carcinoma,91.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"55% CC, 36% MC, 9% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1469,English,Workup Completely or Mostly Correct,Ecthyma,91.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1470,English,Per-Case Inclusion in diagnosis,Psoriasis,100.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1471,English,Correct diagnosis (top Dx),Psoriasis,100.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1472,English,Top Diagnosis Matched,Viral exanthem,100.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 60%; Differential Diagnosis: 18% CC, 73% MC, 9% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1473,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Psoriasis,100.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 0% CC, 82% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1474,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Basal Cell Carcinoma,100.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 40% 2/5; Differential Diagnosis: 55% CC, 36% MC, 9% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1475,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Syphilis,100.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 27% CC, 55% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1476,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Tinea corporis,100.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 60%  3/5; Differential Diagnosis: 0% CC, 45% MC, 55% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1477,English,Differential Diagnosis Completely Completely Correct or Mostly Correct,Melanoma,100.00%,5,ChatGPT 4,ChatGPT-4,3/14/2023,"Accuracy Rate: 100%; Differential Diagnosis: 27% CC, 55% MC, 18% PC, 0% MI","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1478,English,Workup Completely or Mostly Correct,Viral exanthem,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1479,English,Workup Completely or Mostly Correct,Psoriasis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1480,English,Workup Completely or Mostly Correct,Basal Cell Carcinoma,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1481,English,Workup Completely or Mostly Correct,Syphilis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1482,English,Workup Completely or Mostly Correct,cutaneous T-cell lymphoma,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1483,English,Workup Completely or Mostly Correct,Tinea corporis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1484,English,Workup Completely or Mostly Correct,Melanoma,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,Likert scale evaluation of appropriateness of ChatGPT4’s recommended clinical workup by vignette (N=11),"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1485,English,Viral exanthem - Differential Diagnosis Completely or Mostly Correct,Viral exanthem,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1486,English,Atopic Dermatitis - Differential Diagnosis Completely or Mostly Correct,Atopic dermatitis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1487,English,"PsO, psoriasis - Differential Diagnosis Completely or Mostly Correct",Psoriasis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1488,English,"BCC, basal cell carcinoma - Differential Diagnosis Completely or Mostly Correct",Basal Cell Carcinoma,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1489,English,Syphilis - Differential Diagnosis Completely or Mostly Correct,Syphilis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1490,English,Tinea Corporis - Differential Diagnosis Completely or Mostly Correct,Tinea corporis,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1491,English,Melanoma    - Differential Diagnosis Completely or Mostly Correct,Melanoma,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1492,English,"PsO, psoriasis diagnosis",Psoriasis,100.00%,1,ChatGPT 4,ChatGPT-4,3/14/2023,Completely or Mostly Correct Workup,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1493,English,Melanoma diagnosis/ChatGPT4 Dx matching top dermatologist Dx,Melanoma,100.00%,1,ChatGPT 4,ChatGPT-4,3/14/2023,Completely or Mostly Correct Workup,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1494,English,"BCC, basal cell carcinoma  diagnosis/ChatGPT4 Dx matching top dermatologist Dx",Basal Cell Carcinoma,100.00%,1,ChatGPT 4,ChatGPT-4,3/14/2023,Completely or Mostly Correct Workup,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1495,English,Viral exanthem  diagnosis/ChatGPT4 Dx matching top dermatologist Dx,Viral exanthem,100.00%,1,ChatGPT 4,ChatGPT-4,3/14/2023,Completely or Mostly Correct Workup,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1496,English,Syphilis diagnosis/ChatGPT4 Dx matching top dermatologist Dx,Syphilis,100.00%,1,ChatGPT 4,ChatGPT-4,3/14/2023,Completely or Mostly Correct Workup,"Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1497,English,Correct diagnosis rate,Atopic Dermatitis,"60% assumed, table missing values",1,ChatGPT 4,ChatGPT-4,3/14/2023,"ChatGPT4 was rated as providing a completely correct workup an average of 62% of the time for any given clinical vignette and it was rated as providing a completely correct, or mostly correct workup in all vignettes except for those describing atopic dermatitis, Merkel cell carcinoma, and ecthyma. In these conditions, it provided a partially correct recommended workup according to 18% (N=2, atopic dermatitis and Merkel cell carcinoma) and 9% (N=1, ecthyma) of dermatologists’ evaluations.","Ten clinical vignettes describing common dermatologic conditions from the perspective of a non-dermatologist physician, generated by students and an intern. Three board-certified dermatology faculty members independently created differential diagnosis lists for each vignette, listing three to five potential diagnoses ordered from most to least likely. These lists were validated and consolidated by a third dermatologist to form the final expert differential diagnosis list for each vignette. Eleven board-certified dermatologists rated the accuracy of ChatGPT-4's diagnoses and the appropriateness of its recommended workups using predefined Likert scales","Greif C, Mpunga N, Koopman IV, Pye A, Hivnor CM, Owen JL. Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions. Dermatology Online Journal. 2024;30(4).",Evaluating the effectiveness of ChatGPT4 in the diagnosis and workup of dermatologic conditions,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,43
1498,English,Evidence Synthesis in Self-management (What is the effectiveness of online behavioural interventions to support eczema self-management?),Eczema,60.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"Score used as proxy for accuracy: On a scale of 0 to 100% where 0 signifies that the references had no relationship with the study question and 100% is for the three most pertinent references for this topic, how would you rate the references provided? Factual error: minor",20 questions derived from 5 high-impact peer-reviewed research publications by the corresponding authors,"Gravel J, D’Amours-Gravel M, Osmanlliu E. Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions. medRxiv. 2023:2023–03 Supplemental mterial:https://ars.els-cdn.com/content/image/1-s2.0-S2949761223000366-mmc1.pdf  Now peer-reviewed: Gravel J, D’Amours-Gravel M, Osmanlliu E. Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions. Mayo Clinic Proceedings: Digital Health. 2023 Sep 1;1(3):226-34.",Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions,Patient Education Materials and Readability Studies,Patient Education,2023,OpenAI GPT series,54
1499,English,Wound Care: addressing concerns related to skin breakdown and pressure ulcers in the context of awake prone positioning in non-intubated adults with hypoxemic respiratory failure,Ulcers,70.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"Score used as proxy for accuracy: On a scale of 0 to 100% where 0 signifies that the references had no relationship with the study question and 100% is for the three most pertinent references for this topic, how would you rate the references provided? Factual error: No",20 questions derived from 5 high-impact peer-reviewed research publications by the corresponding authors,"Gravel J, D’Amours-Gravel M, Osmanlliu E. Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions. medRxiv. 2023:2023–03 Supplemental mterial:https://ars.els-cdn.com/content/image/1-s2.0-S2949761223000366-mmc1.pdf  Now peer-reviewed: Gravel J, D’Amours-Gravel M, Osmanlliu E. Learning to fake it: limited responses and fabricated references provided by ChatGPT for medical questions. Mayo Clinic Proceedings: Digital Health. 2023 Sep 1;1(3):226-34.",Learning to Fake It: Limited Responses and Fabricated References Provided by ChatGPT for Medical Questions,Patient Education Materials and Readability Studies,Patient Education,2023,OpenAI GPT series,54
1500,English,"Readability of Information, patient education",Hidradenitis suppurativa,28.70%,9,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"Flesch Reading Ease Score: 28.7 (indicates difficult readability; on the scale of 100). - Grade Level: 7-9 grades above recommended. While we know they collected content from 55 web pages and posed questions covering FAQ content, the total number of distinct questions posed to ChatGPT is not reported in the paper. Since on the official Hidradenitis Suppurativa Foundation (HSF) FAQ page, there are 9 distinct FAQs, assuming that sample size is 9","Responses to frequently asked questions from the HS Foundation (HSF), HS Patient Guide (HSPG), and ChatGPT-3.5, along with HS-related websites (Google, Yahoo, and Bing were searched using the term “hidradenitis suppurativa”). The top 50 web pages from each search engine were reviewed, of which, 55 met inclusion criteria for further analysis.","Gawey L, Dagenet CB, Tran KA, Park S, Hsiao JL, Shi V. Readability of Information Generated by ChatGPT for Hidradenitis Suppurativa. JMIR Dermatol. 2024 Aug 14;7:e55204. doi: 10.2196/55204. PMID: 39141908; PMCID: PMC11358659.",Readability of Information Generated by ChatGPT for Hidradenitis Suppurativa,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,53
1501,English,Image-based diagnostics,Shingles,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"""I see so many successes, and have experienced my own, but I wanted to point out as many others have that ChatGPT is not infallible and makes mistakes. My husband had a weird rash and I took photos and explained it to chat GPT. It said it was ringworm and we kind of took that at face value until we could see his doctor. The doctor said it was definitely not ringworm and was textbook shingles. There is treatment if shingles is treated within 72 hours. My husband wouldn't have been within that window no matter what, but giving the wrong diagnosis could possibly cause people to delay treatment. ChatGPT does not diagnose in a proper triage or differential manner, even if asked to directly. It will give you the most likely answer. Which can be wrong. Will I continue to use Chat GPT for medical issues? Probably. Will I trust it? Only as much as I would trust asking my neighbour (the one who isnt a doctor) for a medical opinion. ChatGPT does not replace medical care. Be careful out there. Edit: We did also describe symptoms and asked it to ask questions to accurately assess. I gave it other contextual information too. We also sought other information from other sources. I don't live in a vacuum. Some people here make a lot of assumptions. I did not make any decisions any differently than I would have without ChatGPT. This was all done out of interest. Also, I was never going to fully accept the answer without a medical professional's confirmation. The point of this post is to share the outcome from chatgpt compared to the diagnosis of a medical professional, and discourage anyone thinking about using ChatGPT in place of medical advice.]""",Reddit Posts,"Gabashvili, Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis, 2025",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Patient Education,2025,OpenAI GPT series,88
1502,English,Diagnostics based on bloodwork and symptom,"Chronic urticaria and angioedema, potentially autoimmune-related (Hashimoto)",100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"""March 29, 2025, I left my son’s baby shower party and was giving a ride home to my wife’s aunt. I stopped at the gas station to fill up the tank and as soon as I seat back inside the car, I started to feel an itch on my right shoulder. Didn’t really think much about it, but okay, no problem, I scratch, get some relief and start driving. During the 20 minute trip, my itchiness starts to get intense, it is spreading down on my arm and to my chest. Eventually I stop the car, remove my shirt and I am seeing huge areas of redness and hives. This was the beginning of my nightmare. Fast forward to Monday, I wake up at 3 am, my lips were very swollen, my breathing had this whistle sound, and I couldn’t even fully open my eyes. I was seen by a doctor in less than 20 minutes at the closest ER. He said that since I am Type 1 diabetic, is not unusual for people with T1D to have chronic urticaria caused by my immune system, so he prescribed me with a 5 day course of prednisone (50mg, once daily), administered an epi pen and gave me a prescription for 4x pills per day of 20mg Blexten, I was then sent home and much better feeling. The next Monday, my symptoms slowly but surely came back. I go with a swollen face to the ER, where I was given a epi pen and two Reactine pills once more. The doctored ordered bloodwork to be done and sent me home, stating that it would take a few days to get the results, but that he would let me know the results on the testing that could be done right there and then. A lot of my results came back abnormal, showing significant inflammation and high levels of infection. I come home feeling better, but not 100%. After this, things escalate incredibly quickly. The doctor ordered bloodwork at the ER also made a referral to an allergy specialist which I was able to two days after this ER visit. I get home, and decide to use ChatGPT to try and see what really was going on with the results I had on hand from the bloodwork I have done at the ER. To my surprise, I get a diagnosis by ChatGPT of Hashimoto disease. During the next few days, my hives and angioedema get much worse to the point where I can’t walk around dressed, so I had to stop work and stay home naked. No medication I took that has been prescribed has helped. Saturday, April 19, 2025 things become even worse. I wake up and my whole neck was incredibly swelled and very painful. My skin is incredibly hot and my skin is sensitive to touch. I keep giving my symptoms to GPT which insists on Hashimoto and that I need to rush back to hospital, which I did. There I was prescribed another 5 day course of prednisone (50mg, daily) and to also take 4x 20mg Blexten + 4x 10mg Reactine daily. This has no effect. My neck remains swollen and I call Monday (Easter Monday) to the specialist clinic, over and over again, leaving voicemails that I am not well and I am suffering, that none of the medication was working. Finally, I was able to speak to them today, April 22, 2025 first thing in the morning. I was able to get an appointment at 9AM, but the doctor dismissed my concerns about potentially having Hashimoto, that this was caused mainly because of my T1D and that the only course of treatment at this point was to change to 10mg Rupal daily instead of Blexten, but keep taking everything else, saying that Hashimoto was nonsense and he really didn’t believe me. He also has asked for a skin punch biopsy to rule something else entirely as my symptoms are severe and nothing is sorting effect with a follow up scheduled to June. Not even a hour goes by and a part of my bloodwork results come back that showed that my Anti-Thyroid Peroxide was at 600 (normal is less than 40). Following this result, I once again called the specialist, which tells me that this case now is a matter of my endo. My endo called me at 5.30PM and confirmed my Hashimoto diagnosis. A diagnosis that was given by GPT over a week prior to the doctors who refused to believe me. I have been suffering for 3 weeks and just now I was heard. GPT was right since the first time I called. AI is amazing and I truly believe that it will help an incredible amount of people. Thank you ChatGPT for helping me. Technology is a blessing. EDIT: FOR ANYONE WHO WOULD LIKE TO SEE THE PROMPTS. I HAD TO TAKE SCRENSHOTS OF THE WHOLE THING FOR THE PAST COUPLE OF WEEKS. I HAVE NUMBERED FROM 1...43 SO IT IS EASY TO READ IN ORDER. https://www.icloud.com/iclouddrive/008-x6FE4BLDYsBlKI1dYrvcw#GPT_SCREENSHOTS""",Reddit Posts,"Gabashvili, Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis, 2025",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Patient Education,2025,OpenAI GPT series,88
1503,English,Rare conditions knowledge assessment,PATM,75.00%,3,Bard,Bard (linked to the author's google account),3/21/2023,"Correctly defined PATM and cited an authentic source, but included additional hallucinated references.",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
1504,English,Rare conditions knowledge assessment,PATM,0.00%,3,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Hallucination: ""PATM stands for Psychogenic Attributed Misattribution.""",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
1505,English,Rare conditions knowledge assessment,PATM,0.00%,3,ChatGPT o1,ChatGPT o1 Mini (anonymously assessed via LLM arena),5/1/2025,"Hallucination: Defined PATM as either ""Pervasive Auditory-Tactile Misophonia"" or ""People against me.""",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
1506,English,Rare conditions knowledge assessment,PATM,100.00%,3,ChatGPT o1,ChatGPT o1 Preview,5/1/2025,Provided accurate responses consistently.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
1507,English,Rare conditions knowledge assessment,PATM,100.00%,3,ChatGPT 4o,Chatgpt-4o  (anonymously assessed via LLM arena),5/13/2024,Provided accurate responses consistently.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
1508,English,Rare conditions knowledge assessment,PATM,Positive Development. Correct but Unhelpful; Knowledge Gap Acknowledged; Accurate Non Answer,3,Claude 3,Claude 3.5 Sonnet,5/1/2025,acknowledged unfamiliarity with the term requesting additional information rather than risking hallucination.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Claude series,88
1509,English,Rare conditions knowledge assessment,PATM,100.00%,3,Copilot,Copilot,5/1/2025,,Questions based on author's original research,"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
1510,English,Rare conditions knowledge assessment,PATM,100.00%,3,Deepseek,deepseek-v3,5/1/2025,Provided accurate responses consistently.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,DeepSeek's models,88
1511,English,Rare conditions knowledge assessment,PATM,0.00%,3,Gemini 1.5,Gemini 1.5 (anonymously assessed via LLM arena),2/1/2024,"Defined PATM as ""Post-Acute Tolerance to Mycobacteria.""",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
1512,English,Rare conditions knowledge assessment,PATM,0.00%,3,Gemini 1.5,Gemini 1.5 (linked to the author's account),2/1/2024,Highly accurate responses due to familiarity with the author's publications.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
1513,English,Rare conditions knowledge assessment,PATM,0.00%,3,Gemini 1.5,Gemini-1.5-flash-8b-001,2/1/2024,"Hallucination: Defined PATM as ""Post-Acne Telangiectasia and Maules,"" associating it with emotional distress from acne scarring and telangiectasia.",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
1514,English,Rare conditions knowledge assessment,PATM,100.00%,3,Gemini 2.0,gemini-2.0-flash-thinking-exp-1219,5/1/2025,Provided accurate responses consistently.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
1515,English,Rare conditions knowledge assessment,PATM,100.00%,3,Glm,GLM-4-9B (Zhipu AI),5/1/2025,Provided accurate responses consistently.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Zhipu AI family of models,88
1516,English,Rare conditions knowledge assessment,PATM,100.00%,3,Grok,Grok-2-2024-08-13  (anonymously assessed via LLM arena),8/13/2024,Provided accurate responses consistently.,Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,xAI Grok series,88
1517,English,Rare conditions knowledge assessment,PATM,0.00%,3,Llama,LLAMA-3.1,3/1/2024,"Hallucination: Defined PATM as ""Periodic Acne, Tiredness, Malaise, and Migraines,"" but correctly identified potential links with SIBO.",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,LLaMA Series by Meta,88
1518,English,Rare conditions knowledge assessment,PATM,0.00%,3,Mistral,Mistral-large,7/4/2024,"Hallucination: Defined PATM as ""Partially Attenuated Thrush Mycopathy,"" but correctly identified links with Candida.",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Mistral series,88
1519,English,Rare conditions knowledge assessment,PATM,100.00%,3,Perplexity,Perplexity,5/1/2025,Correct,Questions based on author's original research,"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Perplexity,88
1520,English,Rare conditions knowledge assessment,PATM,0.00%,3,Unkn,test-LLM1 (assessed via LLM arena: lmarena.ai)),7/1/2025,"Hallucination: Defined PATM as ""Psychogenic Alopecia with Trichodynia and Multiple hairs"" (also known as Psychogenic Hair Plucking or Trichotillomania with alopecia).",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,unknown (LLM arena),88
1521,English,Rare conditions knowledge assessment,PATM,0.00%,3,Unkn,test-LLM2 (assessed via LLM arena: lmarena.ai)),7/1/2025,"Hallucination: Defined PATM as ""People with Auditory, Tactile, and Motion Sensations.""",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,unknown (LLM arena),88
1522,English,Rare conditions knowledge assessment,PATM,60.00%,3,Yi,Yi-Lightning (01.AI),10/1/2024,"Defined PATM as ""Poor Hygiene, Anxiety, and Trapped Microbes,"" though correctly associated it with TMAU.",Questions based on author's original research (eg: what is microbial basis of PATM),"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,"Yi series, 01.AI ",88
1523,English,Rare conditions knowledge assessment,PATM,Correct but Unhelpful; Knowledge Gap Acknowledged; Accurate Non Answer,3,Nova,amazon-nova-experimental-chat-05-14,7/1/2025,"""There is no widely recognized or medically validated condition known as ""PATM"" in current medical literature, databases, or diagnostic manuals such as the ICD-11 (International Classification of Diseases, 11th Revision) or the DSM-5-TR (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, Text Revision). Given that ""PATM"" is described as a ""patient-coined condition,"" it likely originated from a patient or a small group of patients rather than from the medical or scientific community.""",General questions about PATM,"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,Amazon models,88
1524,English,Rare conditions knowledge assessment,PATM,80.00%,3,Unkn,triangle,7/1/2025,"""PATM (which stands for ""People Are Talking About Me"" or sometimes ""People Are Talking About Me"" syndrome) is a patient-coined condition characterized by the persistent belief that one’s body emits an invisible substance (e.g., odor, pheromones, or airborne particles) that causes others to react negatively—such as coughing, sneezing, clearing throats, moving away, or whispering. It is not officially recognized in medical diagnostic manuals (like the DSM-5 or ICD-11) but has been described anecdotally by individuals worldwide, often in online communities.""",General questions about PATM,"Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Clinical Practice,2025,unknown (LLM arena),88
1525,English,Image-based diagnostics,Psychodermatology,0.00%,2,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1526,English,Image-based diagnostics,Skin surgery,60.00%,5,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1527,English,Cutaneous allergy,Cutaneous allergy,83.00%,6,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1528,English,Formulation and systemic therapy,Therapy,88.00%,8,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1529,English,Dermatopathology,Dermatopathology,88.00%,8,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1530,English,Skin oncology,Skin oncology,88.00%,16,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1531,English,"Dermatology, Specialty Certificate Examination (SCE)",Certification,90.00%,100,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1532,English,General dermatology,General dermatology,95.00%,22,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1533,English,Skin of colour,Skin of color,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1534,English,Skin biology and research,Skin biology and research,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1535,English,Dressings and wound care,"Wounds, Dressings",100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1536,English,Photodermatology,Photodermatology,100.00%,3,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1537,English,Genitourinary medicine,Genitourinary medicine,100.00%,3,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1538,English,Image analysis of exam questions,"Images, Dermatology Certification",100.00%,4,ChatGPT 4o,ChatGPT-4o,5/13/2024,"The low number of questions involving images means that it is not possible to infer their overall performance as image analysis tools. Current iterations of LLMs are at least able to incorporate image analysis as part of their reasoning. Without the images, LLMs attempted to offer a description of each of the available multiple-choice answers, including the epidemiology and macroscopic description of the lesion. Four image-based questions were assessed, with two to five LLMs answering correctly for each of the questions. Only Claude and Copilot provided a caveat on the potential for AI-generated content being incorrect.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1539,English,Infectious disease,Infectious disorders,100.00%,9,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1540,English,Paediatrics and genetics,"Pediatric Dermatology, Genetics",100.00%,15,ChatGPT 4o,ChatGPT-4o,5/13/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1541,English,Image-based diagnostics,Psychodermatology,50.00%,2,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1542,English,Image-based diagnostics,Skin surgery,60.00%,5,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1543,English,Image-based diagnostics,Skin oncology,75.00%,16,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1544,English,Cutaneous allergy,Cutaneous allergy,83.00%,6,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1545,English,Formulation and systemic therapy,Therapy,88.00%,8,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1546,English,Infectious disease,Infectious disorders,89.00%,9,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1547,English,General dermatology,General dermatology,91.00%,22,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1548,English,Paediatrics and genetics,"Pediatric Dermatology, Genetics",93.00%,15,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1549,English,Skin of colour,Skin of color,100.00%,1,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1550,English,Skin biology and research,Skin biology and research,100.00%,1,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1551,English,Dressings and wound care,"Wounds, Dressings",100.00%,1,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1552,English,Photodermatology,Photodermatology,100.00%,3,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1553,English,Genitourinary medicine,Genitourinary medicine,100.00%,3,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1554,English,Dermatopathology,Dermatopathology,100.00%,8,Claude 3,Claude 3.5-Sonnet,6/20/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1555,English,"Dermatology, Specialty Certificate Examination (SCE)",Certification,87.00%,100,Claude 3,"Claude-3.5 Sonnet (Anthropic, San Francisco, CA, USA)",6/20/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Claude series,30
1556,English,Image-based diagnostics,Skin of Color,0.00%,1,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1557,English,Image-based diagnostics,Cutaneous allergy,67.00%,6,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1558,English,Skin surgery,Skin surgery,80.00%,5,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1559,English,Paediatrics and genetics,"Pediatric Dermatology, Genetics",80.00%,15,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1560,English,Formulation and systemic therapy,Therapy,88.00%,8,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1561,English,Dermatopathology,Dermatopathology,88.00%,8,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1562,English,Skin oncology,Skin oncology,88.00%,16,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1563,English,"Dermatology, Specialty Certificate Examination (SCE)",Certification,88.00%,100,Copilot,Copilot,5/1/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1564,English,General dermatology,General dermatology,95.00%,22,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1565,English,Skin biology and research,skin biology,100.00%,1,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1566,English,Dressings and wound care,Wounds,100.00%,1,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1567,English,Psychodermatology,Psychodermatology,100.00%,2,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1568,English,Photodermatology,Photodermatology,100.00%,3,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1569,English,Genitourinary medicine,Genitourinary,100.00%,3,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1570,English,Infectious disease,skin infections,100.00%,9,Copilot,Copilot,5/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,30
1571,English,Image-based diagnostics,Skin biology and research,0.00%,1,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1572,English,Image-based diagnostics,Psychodermatology,0.00%,2,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1573,English,Image-based diagnostics,Skin surgery,40.00%,5,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1574,English,Image-based diagnostics,Skin oncology,63.00%,16,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1575,English,Image-based diagnostics,Photodermatology,67.00%,3,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1576,English,Image-based diagnostics,Infectious disorders,67.00%,9,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1577,English,Image-based diagnostics,"Pediatric Dermatology, Genetics",73.00%,15,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1578,English,"Dermatology, Specialty Certificate Examination (SCE)",Certification,75.00%,100,Gemini 1.0,Gemini 1.0,2/1/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1579,English,Cutaneous allergy,Cutaneous allergy,83.00%,6,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1580,English,General dermatology,General dermatology,86.00%,22,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1581,English,Dermatopathology,Dermatopathology,88.00%,8,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1582,English,Skin of colour,Skin of color,100.00%,1,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1583,English,Dressings and wound care,Wounds,100.00%,1,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1584,English,Genitourinary medicine,Genitourinary,100.00%,3,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1585,English,Formulation and systemic therapy,Therapy,100.00%,8,Gemini 1.0,Gemini 1.0,2/1/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Google's Family of LLMs,30
1586,English,Formulation and systemic therapy,Therapy,75.00%,8,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1587,English,Skin oncology,Skin oncology,75.00%,16,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1588,English,Skin surgery,Skin surgery,80.00%,5,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1589,English,Paediatrics and genetics,"Pediatric Dermatology, Genetics",80.00%,15,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1590,English,Cutaneous allergy,Cutaneous allergy,83.00%,6,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1591,English,"Dermatology, Specialty Certificate Examination (SCE)",Certification,87.00%,100,Perplexity,Perplexity,7/4/2024,"The accuracies for Claude-3.5 Sonnet, Copilot, Gemini, ChatGPT-4o, and Perplexity were 87, 88, 75, 90, and 87, respectively (p = 0.023). Each of the 100 questions and their respective five multiple-choice answers were inputted into LLMs individually. The responses of each LLM were recorded and compared against the standard answer. On July 24, 2024, Four multiple-choice options included photographic reference material and images were uploaded in their original resolution alongside the clinical text/question in the same prompt.","100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1592,English,Infectious disease,Infectious disorders,89.00%,9,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1593,English,General dermatology,General dermatology,95.00%,22,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1594,English,Skin of color,Skin of color,100.00%,1,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1595,English,Skin biology and research,Skin biology and research,100.00%,1,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1596,English,Dressings and wound care,"Wounds, Dressings",100.00%,1,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1597,English,Psychodermatology,Psychodermatology,100.00%,2,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1598,English,Photodermatology,Photodermatology,100.00%,3,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1599,English,Genitourinary medicine,Genitourinary medicine,100.00%,3,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1600,English,Dermatopathology,Dermatopathology,100.00%,8,Perplexity,Perplexity,7/4/2024,,"100 multiple-choice questions, including 4 image-based questions, extracted from the Specialty Certificate Examination (SCE) in Dermatology sample questions provided by the Membership of the Royal Colleges of Physicians of the United Kingdom. These questions are considered an accurate representation of the actual SCE in Dermatology exam.","Fan KS, Fan KH. Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology. Dermato. 2024 Sep 30;4(4):124-35. https://doi.org/10.3390/dermato4040013",Dermatological Knowledge and Image Analysis Performance of Large Language Models Based on Specialty Certificate Examination in Dermatology,Dermatology Examinations and Practice Questions,Professional Education,2024,Perplexity,30
1601,French,"Hidradenitis suppurativa (HS), patient education, French",Hidradenitis suppurativa,14.00%,126,Bard,Bard,3/21/2023,"Responses by ChatGPT to 6 of the 7 queries were deemed “appropriate” (86%) by the experts, whereas the response to one question (14%) was rated inappropriate (Q6 “What treatments are available?”). Responses by Bard to 1 of the 7 queries were deemed “appropriate” (14%) by the experts (Q5 “Which doctor do I need to see?”), whereas responses to 86% (n=6), were rated inappropriate.","Seven questions related to HS were developed with the help of HS patient associations. All questions were asked to both AI systems on the same day (December 20, 2023) in French for practicality. The ChatGPT and Bard responses were independently assessed by 18 hS experts. All experts independently evaluated all responses using a 5-point Likert scale (1: strongly agree; 2: agree; 3: neutral; 4: disagree; 5: strongly disagree).","Ezanno AC, Fougerousse AC, Pruvost-Balland C, Maccari F, Fite C; ResoVerneuil. AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information. Clin Cosmet Investig Dermatol. 2024 Nov 2;17:2459-2464. doi: 10.2147/CCID.S478309. PMID: 39507766; PMCID: PMC11539865.",AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,45
1602,French,"Hidradenitis suppurativa (HS), patient education, French",Hidradenitis suppurativa,86.00%,126,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Responses by ChatGPT to 6 of the 7 queries were deemed “appropriate” (86%) by the experts, whereas the response to one question (14%) was rated inappropriate (Q6 “What treatments are available?”). Responses by Bard to 1 of the 7 queries were deemed “appropriate” (14%) by the experts (Q5 “Which doctor do I need to see?”), whereas responses to 86% (n=6), were rated inappropriate.","Seven questions related to HS were developed with the help of HS patient associations. All questions were asked to both AI systems on the same day (December 20, 2023) in French for practicality. The ChatGPT and Bard responses were independently assessed by 18 hS experts. All experts independently evaluated all responses using a 5-point Likert scale (1: strongly agree; 2: agree; 3: neutral; 4: disagree; 5: strongly disagree).","Ezanno AC, Fougerousse AC, Pruvost-Balland C, Maccari F, Fite C; ResoVerneuil. AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information. Clin Cosmet Investig Dermatol. 2024 Nov 2;17:2459-2464. doi: 10.2147/CCID.S478309. PMID: 39507766; PMCID: PMC11539865.",AI in Hidradenitis Suppurativa: Expert Evaluation of Patient-Facing Information,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,45
1603,English,"Herpes zoster infection, commonly known as shingles; medical drug repurposing",Herpes zoster,100.00%,10,ChatGPT 3.5,ChatGPT-3.5,11/1/2022,All questions about reusing medications to treat herpes zoster can be answered by ChatGPT,10 pharmacological repurposing questions considered relevant to the treatment of herpes zoster virus infection,"Daungsupawong H, Wiwanitkit V. Exploring Drug Repurposing Through Artificial Intelligence: A Novel Approach to Treating Herpes Zoster Infection. Mustansiriya Medical Journal. 2024 Jan 1;23(1):29-33.",Exploring Drug Repurposing Through Artificial Intelligence: A Novel Approach to Treating Herpes Zoster Infection,Medication Recommendations and Treatment Efficacy,Clinical Practice,2024,OpenAI GPT series,23
1604,English,Melanoma detection from macroscopic images,Melanoma,45.00%,3,Llava,LLAVA (Large Language and Vision Assistant),7/12/2024,"Patient demographics influenced ChatGPT treatment recommendations, raising concerns about potential inequities in GenAI-driven medical guidance","3 macroscopic images (900 × 1100 pixels; 96-dpi resolution) of melanomas (malignant) and melanocytic nevi (benign) obtained from the publicly available and validated MClass-D data set [6], Dermnet NZ, and dermatology textbooks ","McDarby M, Mroz EL, Hahne J, Malling CD, Carpenter BD, Parker PA. “Hospice Care Could Be a Compassionate Choice”: ChatGPT Responses to Questions About Decision Making in Advanced Cancer. Journal of Palliative Medicine. 2024 Dec 1;27(12):1618-24.; Cirone K, Akrout M, Abid L, Oakley A. Assessing the Utility of Multimodal Large Language Models (GPT-4 Vision and Large Language and Vision Assistant) in Identifying Melanoma Across Different Skin Tones. JMIR Dermatol. 2024 Mar 13;7:e55508. doi: 10.2196/55508. PMID: 38477960; PMCID: PMC10973960.",“Hospice Care Could Be a Compassionate Choiceâ€�: ChatGPT Responses to Questions About Decision Making in Advanced Cancer,Medical Records and Diagnostic Processes,Clinical Practice,2024,LLAVA,41
1605,English,Melanoma detection from macroscopic images,Melanoma,85.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,"Patient demographics influenced ChatGPT treatment recommendations, raising concerns about potential inequities in GenAI-driven medical guidance","3 macroscopic images (900 × 1100 pixels; 96-dpi resolution) of melanomas (malignant) and melanocytic nevi (benign) obtained from the publicly available and validated MClass-D data set [6], Dermnet NZ, and dermatology textbooks ","McDarby M, Mroz EL, Hahne J, Malling CD, Carpenter BD, Parker PA. “Hospice Care Could Be a Compassionate Choice”: ChatGPT Responses to Questions About Decision Making in Advanced Cancer. Journal of Palliative Medicine. 2024 Dec 1;27(12):1618-24.; Cirone K, Akrout M, Abid L, Oakley A. Assessing the Utility of Multimodal Large Language Models (GPT-4 Vision and Large Language and Vision Assistant) in Identifying Melanoma Across Different Skin Tones. JMIR Dermatol. 2024 Mar 13;7:e55508. doi: 10.2196/55508. PMID: 38477960; PMCID: PMC10973960.",“Hospice Care Could Be a Compassionate Choiceâ€�: ChatGPT Responses to Questions About Decision Making in Advanced Cancer,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,41
1606,English,Multilogical reasoning,Multilogical reasoning,71.40%,21,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1607,English,"Continuing Education, multiple choice questions",Certification,80.40%,184,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1608,English,Simple reasoning,Simple reasoning,81.20%,85,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1609,English,Knowledge Recall,Knowledge Recall,82.10%,78,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1610,English,No omission of relevant content,No omission of relevant content,85.00%,30,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1611,English,Correct Reasoning ,Logical reasoning,91.70%,30,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1612,English,No Harm Evidence,No Harm Evidence,93.30%,30,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1613,English,Correct Comprehension,Comprehension,95.00%,30,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1614,English,No Bias Evidence,No Bias Evidence,100.00%,30,ChatGPT 4,ChatGPT-4,3/14/2023,Dermatology Continuing Medical Education ,"184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1615,English,Hair Shedding Medication,Hair Shedding,Fail,1,ChatGPT 4,ChatGPT-4,3/14/2023,Failure to recommend decreasing the oral isotretinoin dose after new-onset diffuse hair shedding four months after initiating isotretinoin (Dermatology Continuing Medical Education ),,"Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1616,English,Ecchymosis due to Child Abuse and Neglect,Ecchymosis,Fail,1,ChatGPT 4,ChatGPT-4,3/14/2023,Failure to recommend follow-up for a child where possible abuse-related ecchymosis could not be ruled out (Dermatology Continuing Medical Education ),,"Chen, M.L.; Cai, Z. Ran; Kim, J.; Novoa, R.; Barnes, L.A.; Beam, A.; Linos, E.  140 Performance and risk of harm of a large language model on dermatology continuing medical education questions 2024. Journal of Investigative Dermatology, Volume 144, Issue 8, S25",140 Performance and risk of harm of a large language model on dermatology continuing medical education questions,Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,38
1617,English,"Dermatitis (atopic dermatitis, seborrheic dermatitis, psoriasis, intertrigo, venous stasis dermatitis, and asteatotic dermatitis)",Atopic dermatitis,78.67%,36,Bard,Bard,3/21/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,34
1618,English,Dermatology handouts: Understandability at a sixth grade reading level,Understandability,81.50%,108,Bard,Bard,3/21/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) . 6 dermatologists×18 topics=1086 dermatologists×18 topics=108 total rankings.","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,34
1619,English,"Dyspigmentation (melasma, postinflammatory hyperpigmentation, vitiligo, progressive macular hypomelanosis, lichen planus pigmentosus, and exogenous ochronosis)",Dyspigmentation,82.83%,36,Bard,Bard,3/21/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,34
1620,English,"Alopecia (telogen effluvium, alopecia areata, traction alopecia, central centrifugal cicatricial alopecia, lichen planopilaris, and androgenic alopecia)",Central centrifugal cicatricial alopecia,83.00%,36,Bard,Bard,3/21/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,Google's Family of LLMs,34
1621,English,"Alopecia (telogen effluvium, alopecia areata, traction alopecia, central centrifugal cicatricial alopecia, lichen planopilaris, and androgenic alopecia)",Central centrifugal cicatricial alopecia,86.50%,36,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1622,English,"Alopecia (telogen effluvium, alopecia areata, traction alopecia, central centrifugal cicatricial alopecia, lichen planopilaris, and androgenic alopecia)",Central centrifugal cicatricial alopecia,87.08%,36,Bing,BingAI (GPT-4/Prometheus),3/16/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1623,English,"Dermatitis (atopic dermatitis, seborrheic dermatitis, psoriasis, intertrigo, venous stasis dermatitis, and asteatotic dermatitis)",Atopic dermatitis,88.00%,36,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1624,English,Dermatology handouts: Understandability at a sixth grade reading level,Understandability,88.31%,108,Bing,BingAI (GPT-4/Prometheus),3/16/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) . 6 dermatologists×18 topics=1086 dermatologists×18 topics=108 total rankings.","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1625,English,"Dermatitis (atopic dermatitis, seborrheic dermatitis, psoriasis, intertrigo, venous stasis dermatitis, and asteatotic dermatitis)",Atopic dermatitis,88.83%,36,Bing,BingAI (GPT-4/Prometheus),3/16/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1626,English,"Dyspigmentation (melasma, postinflammatory hyperpigmentation, vitiligo, progressive macular hypomelanosis, lichen planus pigmentosus, and exogenous ochronosis)",Dyspigmentation,89.00%,36,Bing,BingAI (GPT-4/Prometheus),3/16/2023,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1627,English,Dermatology handouts: Understandability at a sixth grade reading level,Understandability,89.53%,108,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) . 6 dermatologists×18 topics=108 total rankings.","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1628,English,"Dyspigmentation (melasma, postinflammatory hyperpigmentation, vitiligo, progressive macular hypomelanosis, lichen planus pigmentosus, and exogenous ochronosis)",Dyspigmentation,94.08%,36,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,,"18 topics across 3 categories (dermatitis, alopecia, dyspigmentation) (total 54 handouts) ","Chang CT, Ticknor IL, Spinelli JA, Bhatia BK, Marwaha S, Mirmirani P, Seidler AM, Man JR, McCleskey PE. Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study. JAAD Int. 2024 Feb 21;15:152-154. doi: 10.1016/j.jdin.2024.02.010. PMID: 38571697; PMCID: PMC10988028.",Comparison of large language models in generating patient handouts for the dermatology clinic: A blinded study,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,34
1629,English,Dermatological Medications - Patient Information Leaflets (PILs),Medications,12.50%,8,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"While 64.6% of condition-related PILs were considered acceptable for patient distribution, this was only 12.5% for medication-related PILs. Dermatologist responses varied based on PIL subject and were positive for structure, relevance and accuracy in 98%, 88% and 65% of responses, respectively, for condition-related PILs, whereas positive responses were seen in only 81%, 38% and 25% for medication-related PILs. ","8 PILs generated by ChatGPT where a BAD-produced PIL was not available. AI-generated PILs were reviewed by eight dermatologists and five nonmedical reviewers using a five-point Likert scale to assess document structure, accuracy, relevance and utility. ","Callum Verran, Michael R Ardern-Jones, Ella Seccombe, BT23 Artificial intelligence generation of patient information leaflets can have a useful role in the dermatology clinic, British Journal of Dermatology, Volume 191, Issue Supplement_1, July 2024, Page i199, https://doi.org/10.1093/bjd/ljae090.420",BT23 Artificial intelligence generation of patient information leaflets can have a useful role in the dermatology clinic,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,21
1630,English,"Dermatological Conditions, Common - Patient Information Leaflets (PILs)",Common conditions,64.60%,8,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"While 64.6% of condition-related PILs were considered acceptable for patient distribution, this was only 12.5% for medication-related PILs. Dermatologist responses varied based on PIL subject and were positive for structure, relevance and accuracy in 98%, 88% and 65% of responses, respectively, for condition-related PILs, whereas positive responses were seen in only 81%, 38% and 25% for medication-related PILs. ","8 PILs generated by ChatGPT where a BAD-produced PIL was not available. AI-generated PILs were reviewed by eight dermatologists and five nonmedical reviewers using a five-point Likert scale to assess document structure, accuracy, relevance and utility. ","Callum Verran, Michael R Ardern-Jones, Ella Seccombe, BT23 Artificial intelligence generation of patient information leaflets can have a useful role in the dermatology clinic, British Journal of Dermatology, Volume 191, Issue Supplement_1, July 2024, Page i199, https://doi.org/10.1093/bjd/ljae090.420",BT23 Artificial intelligence generation of patient information leaflets can have a useful role in the dermatology clinic,Patient Education Materials and Readability Studies,Patient Education,2024,OpenAI GPT series,21
1631,English,Infantile Hemangioma (benign vascular tumor),Infantile Hemangioma,50.00%,4,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1632,English,Nutritional Dermatoses,Nutritional dermatoses,63.60%,11,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1633,English,Physical Injury or Ulcers,injury,70.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1634,English,Multilogical reasoning,Multilogical reasoning,71.40%,21,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1635,English,Pathophysiology,Pathophysiology,71.40%,21,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1636,English,Nonmelanoma skin cancer,Nonmelanoma skin cancer,75.00%,16,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1637,English,Other inflammatory,Inflammation,77.10%,48,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1638,English,Risk factors,Risk factors,79.50%,39,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1639,English,Image-based questions,"Images, Diagnostics",80.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1640,English,"Continuing Education, multiple choice questions",Certification,80.40%,184,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 American Academy of Dermatology quizzes, which grant dermatology continuing medical education credits","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1641,English,Simple reasoning,Simple reasoning,81.20%,85,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1642,English,"Diagnosis, CME","Diagnostics, Certification",81.80%,55,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1643,English,Knowledge Recall,Knowledge Recall,82.10%,78,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1644,English,Treatment,Therapy,82.60%,69,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1645,English,Autoimmune/CTD,"Autoimmune Disorders, Connective Tissue Disease",84.20%,19,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1646,English,Pigmented (benign),"Pigmentary disorders, Benign Lesions",85.70%,7,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1647,English,Pigmented (malignant),"Pigmentary disorders, Cancers",85.70%,21,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1648,English,Alopecia,Alopecia,86.20%,29,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1649,English,Cutaneous Lymphoma,Cutaneous lymphoma,87.50%,8,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1650,English,Skin Infections,Skin infections,100.00%,11,ChatGPT 4,ChatGPT-4,3/14/2023,"Dermatology Continuing Medical Education: LLM exhibit professional-level knowledge, yet low reproducibility of the LLM responses and their inconsistent outcomes, particularly regarding accuracy","184 multiple-choice questions from the October 2021 to September 2023 quizzes of the American Academy of Dermatology, which grants board-certified dermatologists continuing medical education credits for certification maintenance. The date range for eligible questions was chosen on the basis of the ChatGPT-4 training data cutoff of September 2021 during the time of question input.","Cai, Zhuo Ran et al. Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions 2024  Journal of Investigative Dermatology, Volume 144, Issue 8, 1877 - 1879","Assessment of Correctness, Content Omission, and Risk of Harm in Large Language Model Responses to Dermatology Continuing Medical Education Questions",Dermatology Examinations and Practice Questions,Professional Education,2024,OpenAI GPT series,33
1651,English,"Step 2.5, Clinical knowledge, level 5, most difficult questions",Clinical dermatology,13.00%,8,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1652,English,"Step 3.4 (Examinations, Level 4 questions)",Challenging dermatology diagnostics,15.00%,20,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1653,English,"Step 1.5 Basic Science in Dermatology, level 5, most difficult questions",Basic dermatology,20.00%,5,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1654,English,"Step 2.3, Clinical knowledge, level 3",Clinical dermatology,25.00%,48,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1655,English,"Step 3.5 (Examinations, most difficult Level 5 questions)",Expert-level dermatology diagnostics,29.00%,7,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1656,English,"Step 1 with images, USMLE",Basic dermatology,30.00%,53,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1657,English,"Step 1.4 Basic Science in Dermatology, level 4",Basic dermatology,32.00%,19,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1658,English,"Step 2.4, Clinical knowledge, level 4",Clinical dermatology,32.00%,28,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1659,English,"Step 3.3 (Examinations, Level 3 questions)",Advanced Dermatology,33.00%,42,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1660,English,"Step 2 Clinical Knowledge in Dermatology , with images",Clinical dermatology,36.00%,50,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1661,English,"Dermatology questions with images, USMLE","Certification, Images",36.00%,154,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1662,English,"Step 1.3 Basic Science in Dermatology, level 3",Basic dermatology,37.00%,59,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1663,English,"Step 2 Clinical Knowledge in Dermatology (more applied clinical scenarios, requiring the examinee to demonstrate diagnostic skills, clinical reasoning, and knowledge of treatment protocols in dermatology): ability to apply medical knowledge, skills, and understanding of clinical science essential for the provision of patient care under supervision with emphasis on health promotion and disease prevention",Clinical dermatology,38.00%,171,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1664,English,"Step 1.2 Basic Science in Dermatology, level 2",Basic dermatology,39.00%,56,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1665,English,"Step 2 Clinical Knowledge in Dermatology, without images",Clinical dermatology,39.00%,121,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1666,English,"Step 2.2, Clinical knowledge, level 2",Clinical dermatology,40.00%,42,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1667,English,"Step 1 Basic Science in Dermatology, USMLE: foundational science aspects of dermatology, such as the pathology of skin diseases, pharmacology of dermatological treatments, and basic physiological processes related to skin function. Assessment of understanding and ability to apply important concepts of the sciences basic to the practice of medicine, with special emphasis on principles and mechanisms underlying health, disease, and modes of therapy.",skin disease,41.00%,160,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1668,English,Dermatology part of USMLE exam,Certification,41.00%,492,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,"ChatGPT answered fewer correct dermatology-specific questions when compared to its overall performance on the USMLE (41% and 60%, respectively)",492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1669,English,Step 3 with images (Dermatological Examinations),Images,43.00%,51,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1670,English,"Dermatology questions without images, USMLE",Certification,44.00%,338,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1671,English,"Step 1 without images, USMLE",Basic dermatology,46.00%,107,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1672,English,"Step 3 Examinations,  integration of dermatological knowledge into broader medical practice. Step 3 assesses ability to apply medical knowledge and understanding of biomedical and clinical science essential for the unsupervised practice of medicine",Integration,46.00%,161,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1673,English,Step 3 without images (Dermatological Examinations),Diagnostics,47.00%,110,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1674,English,"Step 3.2 (Examinations, Level 2 questions)",Intermediate Dermatology,55.00%,44,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1675,English,"Step 2.1, Clinical knowledge, easiest questions: level 1",Clinical dermatology,58.00%,45,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1676,English,"Step 3.1 (Examinations, easiest Level 1 questions)",Basic dermatology,65.00%,48,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1677,English,"Step 1.1 Basic Science in Dermatology, level 1 questions (easiest)",Basic dermatology,67.00%,21,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,,492 dermatology-related questions from Amboss (online education company that provides resources such as content and questions for board exams and continuing medical education curriculums accredited through the Accreditation Council for Continuing Medical Education. It provides a question bank that prepares medical students for USMLE and National Board of Medical Examiners (NBME) examinations. It has an internal metric for measuring the difficulty of a question based on the number of students who answer it correctly.),"Behrmann J, Hong EM, Meledathu S, Leiter A, Povelaitis M, Mitre M. Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education. Journal of Medical Artificial Intelligence. 2023 Sep 30;6.",Chat generative pre-trained transformer’s performance on dermatology-specific questions and its implications in medical education,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,44
1678,English,ABD-AE-style question generation (American Board of Dermatology Applied Exam),Certification,40.00%,40,ChatGPT 3.5,ChatPDF (GPT-3.5),11/1/2022,"The evaluation encompassed three essential dimensions: accuracy, complexity, and clarity. Of the 40 questions, only 16 (40%) questions created using ChatGPT 3.5 were accurate and at an appropriate level of complexity for a trainee studying for ABD-AE",generation of American Board of Dermatology Applied Exam (ABD-AE)-style questions from continuing medical education articles from the Journal of the American Board of Dermatology,"Ayub I, Hamann D, Hamann CR, Davis MJ. Exploring the Potential and Limitations of Chat Generative Pre-trained Transformer (ChatGPT) in Generating Board-Style Dermatology Questions: A Qualitative Analysis. Cureus. 2023 Aug 18;15(8):e43717. doi: 10.7759/cureus.43717. PMID: 37638266; PMCID: PMC10450251.",Exploring the Potential and Limitations of Chat Generative Pre-trained Transformer (ChatGPT) in Generating Board-Style Dermatology Questions: A Qualitative Analysis,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,7
1679,English,Disorders of hyperpigmentation. Part I. Pathogenesis and clinical features of common pigmentary disorders,Pigmentary disorders,FAIL,1,ChatGPT 3.5,ChatPDF (GPT-3.5),11/1/2022,"The evaluation encompassed three essential dimensions: accuracy, complexity, and clarity. Of the 40 questions, only 16 (40%) questions created using ChatGPT 3.5 were accurate and at an appropriate level of complexity for a trainee studying for ABD-AE",generation of American Board of Dermatology Applied Exam (ABD-AE)-style questions from continuing medical education articles from the Journal of the American Board of Dermatology,"Ayub I, Hamann D, Hamann CR, Davis MJ. Exploring the Potential and Limitations of Chat Generative Pre-trained Transformer (ChatGPT) in Generating Board-Style Dermatology Questions: A Qualitative Analysis. Cureus. 2023 Aug 18;15(8):e43717. doi: 10.7759/cureus.43717. PMID: 37638266; PMCID: PMC10450251.",Exploring the Potential and Limitations of Chat Generative Pre-trained Transformer (ChatGPT) in Generating Board-Style Dermatology Questions: A Qualitative Analysis,Dermatology Examinations and Practice Questions,Professional Education,2023,OpenAI GPT series,7
1680,English,Hard Lump Under the Skin of the Penis,Pityriasis versicolor,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/17/2022,100% preferred the chatbot: 5.00 mean quality score (chatbot) 3.33 mean quality score (physician); 3.33 mean empathy score (chatbot) 1.67 mean empathy score (physician),"195 randomly drawn exchanges from reddit's /AskDocs (a subreddit with approximately 474?000 members where users can post medical questions and verified health care professional volunteers submit answers), ie, a unique member’s question and unique physician’s answer, during October 2022. The original question, including the title and text, was retained for analysis, and the physician response was retained as a benchmark response. Only physician responses were studied because it was expected that physicians’ responses are generally superior to those of other health care professionals or laypersons","Ayers JW, Poliak A, Dredze M, Leas EC, Zhu Z, Kelley JB, Faix DJ, Goodman AM, Longhurst CA, Hogarth M, Smith DM. Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum. JAMA Intern Med. 2023 Jun 1;183(6):589-596. doi: 10.1001/jamainternmed.2023.1838. PMID: 37115527; PMCID: PMC10148230.",Comparing Physician and Artificial Intelligence Chatbot Responses to Patient Questions Posted to a Public Social Media Forum,Patient Education Materials and Readability Studies,Patient Education,2023,OpenAI GPT series,56
1681,English,"Image-based diagnostics, Dark skin","Images, Diagnostics",12.00%,25,ChatGPT 4V,ChatGPT-4V,3/17/2023,"GPT-4 exhibited better performance in providing the correct diagnosis for 77 lighter skin tones (44%, n=11/25) compared to darker skin tones (12%, n=3/25),","Fifty images randomly selected from the Fitzpatrick 17k dataset, a publicly available online collection of clinical images labelled with the appropriate diagnoses and skin types based on the Fitzpatrick scoring system. Half of the images selected represented darker skin tones, Fitzpatrick IV-VI, and the other half represented lighter skin tones, Fitzpatrick I-II. ","Akuffo-Addo E, Samman L, Munawar L, Akbik M, Kokikian N, Wescott R, Wu JJ. Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications. Clin Exp Dermatol. 2024 Sep 18;49(10):1244-1245. doi: 10.1093/ced/llae158. PMID: 38696699.",Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,15
1682,English,"Image-based Diagnostics. Common skin leisons, Top Diagnosis","Images, Diagnostics",28.00%,50,ChatGPT 4V,ChatGPT-4V,3/17/2023,"Overall, GPT-4 correctly diagnosed the condition in 28% of the images (n=14/50), while the correct diagnosis was included in its list of top differentials for 48% of the images (n=24/50)","Fifty images randomly selected from the Fitzpatrick 17k dataset, a publicly available online collection of clinical images labelled with the appropriate diagnoses and skin types based on the Fitzpatrick scoring system. Half of the images selected represented darker skin tones, Fitzpatrick IV-VI, and the other half represented lighter skin tones, Fitzpatrick I-II. ","Akuffo-Addo E, Samman L, Munawar L, Akbik M, Kokikian N, Wescott R, Wu JJ. Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications. Clin Exp Dermatol. 2024 Sep 18;49(10):1244-1245. doi: 10.1093/ced/llae158. PMID: 38696699.",Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,15
1683,English,"Image-based diagnostics, Light skin","Images, Diagnostics",44.00%,25,ChatGPT 4V,ChatGPT-4V,3/17/2023,"GPT-4 exhibited better performance in providing the correct diagnosis for 77 lighter skin tones (44%, n=11/25) compared to darker skin tones (12%, n=3/25),","Fifty images randomly selected from the Fitzpatrick 17k dataset, a publicly available online collection of clinical images labelled with the appropriate diagnoses and skin types based on the Fitzpatrick scoring system. Half of the images selected represented darker skin tones, Fitzpatrick IV-VI, and the other half represented lighter skin tones, Fitzpatrick I-II. ","Akuffo-Addo E, Samman L, Munawar L, Akbik M, Kokikian N, Wescott R, Wu JJ. Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications. Clin Exp Dermatol. 2024 Sep 18;49(10):1244-1245. doi: 10.1093/ced/llae158. PMID: 38696699.",Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,15
1684,English,"Image-based Diagnostics. Common skin leisons , Differential Diagnosis","Images, Diagnostics",48.00%,50,ChatGPT 4V,ChatGPT-4V,3/17/2023,"Overall, GPT-4 correctly diagnosed the condition in 28% of the images (n=14/50), while the correct diagnosis was included in its list of top differentials for 48% of the images (n=24/50)","Fifty images randomly selected from the Fitzpatrick 17k dataset, a publicly available online collection of clinical images labelled with the appropriate diagnoses and skin types based on the Fitzpatrick scoring system. Half of the images selected represented darker skin tones, Fitzpatrick IV-VI, and the other half represented lighter skin tones, Fitzpatrick I-II. ","Akuffo-Addo E, Samman L, Munawar L, Akbik M, Kokikian N, Wescott R, Wu JJ. Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications. Clin Exp Dermatol. 2024 Sep 18;49(10):1244-1245. doi: 10.1093/ced/llae158. PMID: 38696699.",Assessing GPT-4's diagnostic accuracy with darker skin tones: underperformance and implications.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,15
1685,English,"Title for atopic dermatitis case report, total score",Atopic dermatitis,84.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"AI received the highest grade for the title (4.3 on a 5-point star rating scale (1 = terrible, 5 = excellent)), slightly outperforming humans (4.2) and humans collaborating with AI (4.2) .  The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1686,English,"Title for atopic dermatitis case report, score from students",Atopic dermatitis,84.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1687,English,"Title for atopic dermatitis case report, score from residents",Atopic dermatitis,90.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1688,English,"Title for atopic dermatitis case report, score from expert dermatologists",Atopic dermatitis,78.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1689,English,"Short Title for atopic dermatitis case report, Total",Atopic dermatitis,84.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"AI outperformed humans in short title grading. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1690,English,"Short Title for atopic dermatitis case report, STU",Atopic dermatitis,82.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1691,English,"Short Title for atopic dermatitis case report, RES",Atopic dermatitis,86.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1692,English,"Short Title for atopic dermatitis case report, EXP",Atopic dermatitis,86.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1693,English,"Keywords for atopic dermatitis case report, Total",Atopic dermatitis,68.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"AI received the highest grades for keywords, surpassing both humans and humans using AI. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1694,English,"Keywords for atopic dermatitis case report, STU",Atopic dermatitis,72.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1695,English,"Keywords for atopic dermatitis case report, RES",Atopic dermatitis,68.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1696,English,"Keywords for atopic dermatitis case report, EXP",Atopic dermatitis,64.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1697,English,"Abstract for atopic dermatitis case report, Total",Atopic dermatitis,76.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"Human using AI wrote abstract with slightly better scores than AI alone or human alone versions.  The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1698,English,"Abstract for atopic dermatitis case report, STU",Atopic dermatitis,82.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1699,English,"Abstract for atopic dermatitis case report, RES",Atopic dermatitis,66.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1700,English,"Abstract for atopic dermatitis case report, EXP",Atopic dermatitis,74.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1701,English,"Introduction for atopic dermatitis case report, Total",Atopic dermatitis,80.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"AI and humans using AI were equally well rated, both outperforming HUM. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1702,English,"Introduction for atopic dermatitis case report, STU",Atopic dermatitis,88.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1703,English,"Introduction for atopic dermatitis case report, RES",Atopic dermatitis,76.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1704,English,"Introduction for atopic dermatitis case report, EXP",Atopic dermatitis,74.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1705,English,"Discussion for atopic dermatitis case report, Total",Atopic dermatitis,84.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"AI received lower grades for discussion compared to COM and HUM. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1706,English,"Discussion for atopic dermatitis case report, STU",Atopic dermatitis,92.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1707,English,"Discussion for atopic dermatitis case report, RES",Atopic dermatitis,86.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1708,English,"Discussion for atopic dermatitis case report, EXP",Atopic dermatitis,74.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1709,English,"Conclusion for atopic dermatitis case report, Total",Atopic dermatitis,86.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"AI received lower grades for conclusion compared to COM and HUM. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1710,English,"Conclusion for atopic dermatitis case report, STU",Atopic dermatitis,90.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1711,English,"Conclusion for atopic dermatitis case report, RES",Atopic dermatitis,90.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1712,English,"Conclusion for atopic dermatitis case report, EXP",Atopic dermatitis,82.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1713,English,"References for atopic dermatitis case report, Total",Atopic dermatitis,76.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"HUM outperformed AI in references. COM tied with HUM for keywords. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1714,English,"References for atopic dermatitis case report, STU",Atopic dermatitis,86.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1715,English,"References for atopic dermatitis case report, RES",Atopic dermatitis,86.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1716,English,"References for atopic dermatitis case report, EXP",Atopic dermatitis,58.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1717,English,"Scientific Rigor for atopic dermatitis case report, Total",Atopic dermatitis,78.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"COM and AI received equally positive evaluations for scientific rigor and clarity. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1718,English,"Scientific Rigor for atopic dermatitis case report, STU",Atopic dermatitis,86.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1719,English,"Scientific Rigor for atopic dermatitis case report, RES",Atopic dermatitis,80.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1720,English,"Scientific Rigor for atopic dermatitis case report, EXP",Atopic dermatitis,68.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1721,English,"Quality of Writing for atopic dermatitis case report, Total",Atopic dermatitis,80.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"COM outperformed AI in quality of writing, cohesion, and content. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1722,English,"Quality of Writing for atopic dermatitis case report, STU",Atopic dermatitis,90.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1723,English,"Quality of Writing for atopic dermatitis case report, RES",Atopic dermatitis,76.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1724,English,"Quality of Writing for atopic dermatitis case report, EXP",Atopic dermatitis,74.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1725,English,"Clarity for atopic dermatitis case report, Total",Atopic dermatitis,84.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"All LLMs received high clarity scores, with HUM slightly higher. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1726,English,"Clarity for atopic dermatitis case report, STU",Atopic dermatitis,88.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1727,English,"Clarity for atopic dermatitis case report, RES",Atopic dermatitis,86.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1728,English,"Clarity for atopic dermatitis case report, EXP",Atopic dermatitis,80.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1729,English,"Cohesion for atopic dermatitis case report, Total",Atopic dermatitis,78.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"COM and HUM outperformed AI in cohesion. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1730,English,"Cohesion for atopic dermatitis case report, STU",Atopic dermatitis,88.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1731,English,"Cohesion for atopic dermatitis case report, RES",Atopic dermatitis,78.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1732,English,"Cohesion for atopic dermatitis case report, EXP",Atopic dermatitis,68.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1733,English,"Content for atopic dermatitis case report, Total",Atopic dermatitis,82.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"COM received the highest content quality scores, followed by HUM and AI. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1734,English,"Content for atopic dermatitis case report, STU",Atopic dermatitis,92.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1735,English,"Content for atopic dermatitis case report, RES",Atopic dermatitis,82.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1736,English,"Content for atopic dermatitis case report, EXP",Atopic dermatitis,74.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1737,English,"Atopic dermatitis case report, overall quality, Total score",Atopic dermatitis,80.00%,48,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,"COM received the highest content quality scores, followed by HUM and AI. The average grade was calculated based on the 48 individual scores provided by the participants.48 individuals consisted of STU (Students): 17 participants; RES (Residents): 12 participants; EXP (Experts): 19 participants. Participants favored AI-assisted versions (AI and COM) over HUM (P < .001), with COM receiving the highest quality scores. COM and AI achieved 83.8% and 84.3% reduction in writing time, respectively, compared to HUM, while showing 13.9% (P < .001) and 11.1% improvement in quality (P < .001), respectively. However, experts assigned the lowest score for the references of the AI manuscript.","Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1738,English,"Atopic dermatitis case report, overall quality, STU",Atopic dermatitis,88.00%,17,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,17 students,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1739,English,"Atopic dermatitis case report, overall quality, RES",Atopic dermatitis,82.00%,12,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,12 residents,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1740,English,"Atopic dermatitis case report, overall quality, EXP",Atopic dermatitis,74.00%,19,ChatGPT 4 Turbo,ChatGPT-4 Turbo,11/12/2023,19 experts,"Atopic dermatitis case report written by ChatGPT-4 turbo (AI) compared with the same case report written by two human experts (both with MD and PhD degrees), and written by humans assisted with AI (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. ","Bianchi MG, D’adario A, Bianchi PG, Machado BS, Agondi R, Almeida SK, Junior WX, Armelin LM, Aun MV, Bordignon N, Boufleur K. Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences. Journal of Allergy and Clinical Immunology: Global. 2025 Feb 1;4(1):100373. doi: 10.1016/j.jacig.2024.100373. PMID: 39759954; PMCID: PMC11699452.","Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences",Medical Records and Diagnostic Processes.,Professional Education,2025,OpenAI GPT series,58
1741,English,"Original (non?simulated) color, mean accuracy: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",77.70%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1742,English,"Protanopia?simulated (red?deficient), mean accuracy: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",76.00%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1743,English,"Deuteranopia?simulated (green?deficient), mean accuracy: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",73.30%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1744,English,"Tritanopia?simulated (blue?deficient), mean accuracy: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",72.10%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1745,English,"Original (non?simulated) color, accuracy after voting across ten repeats: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",83.80%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"Using a consensus strategy (voting across ten repeats), accuracy increased further. GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1746,English,"Protanopia?simulated (red?deficient), accuracy after voting across ten repeats: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",79.80%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"Using a consensus strategy (voting across ten repeats), accuracy increased further. GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1747,English,"Deuteranopia?simulated (green?deficient), accuracy after voting across ten repeats: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",76.80%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"Using a consensus strategy (voting across ten repeats), accuracy increased further. GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1748,English,"Tritanopia?simulated (blue?deficient), accuracy after voting across ten repeats: Image-based diagnostics; dermoscopic images including Melanoma (skin cancer) and Benign Nevus (Moles)","Images, Diagnostics, Melanoma, Benign Moles",80.80%,100,ChatGPT 4V,ChatGPT-4V,11/17/2023,"Using a consensus strategy (voting across ten repeats), accuracy increased further. GPT?4V can still reliably detect melanoma even if reds, greens, or blues look different. Despite altered color perception, ChatGPT?4V maintained comparable diagnostic accuracy across color vision deficiency (CVD) simulations. ChatGPT?4V can “see” images as someone with a given color vision deficiency might, retaining strong diagnostic performance and providing explanations that reflect the altered color perception. This points to the potential use of GPT?4V as an assistive tool in visually demanding medical fields—especially for learners and practitioners with CVD. Conditions tested were Original (non?simulated) color; Protanopia?simulated (red?deficient); Deuteranopia?simulated (green?deficient); Tritanopia?simulated (blue?deficient). By converting both the “query” images and the “reference” images to the same simulated color space, GPT?4V can maintain consistent color cues and classify lesions more accurately.",100 histopathology?validated dermoscopic images,"Wang J, Yu TC, Kolodney MS, Perrotta PL, Hu G. Adapting ChatGPT for Color Blindness in Medical Education. Annals of Biomedical Engineering. 2024 Nov 27:1-4.  https://link.springer.com/article/10.1007/s10439-024-03656-0, doi: 10.1007/s10439-024-03656-0",Adapting ChatGPT for Color Blindness in Medical Education,,,2024,OpenAI GPT series,59
1749,Spanish,Fernández Huerta Index: Readability level of Patient education materials,"Patient education, Cosmetic dermatology, keloids, scar management and wound healing",93.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,Readability of Spanish PEMs from the Society of Pediatric Dermatology (SPD) was compared with those generated by Open-AI's ChatGPT 4.0 and Google Gemini. AI-generated PEMs could better meet readability standards and potentially improve patient outcomes.,10 questions used to generate Patient education materials (PEMs),"Duran S, Gonzalez AM, Nguyen K, Nguyen J, Zinn Z. Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures. Pediatr Dermatol. 2024 Nov 12. doi: 10.1111/pde.15805. Epub ahead of print. PMID: 39533849.",Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures,,,2024,OpenAI GPT series,57
1750,Spanish,Fernández Huerta Index: Readability level of Patient education materials,"Patient education, Cosmetic dermatology, keloids, scar management and wound healing",90.00%,10,Gemini 1.0,Gemini 1.0,3/17/2023,Readability of Spanish PEMs from the Society of Pediatric Dermatology (SPD) was compared with those generated by Open-AI's ChatGPT 4.0 and Google Gemini. AI-generated PEMs could better meet readability standards and potentially improve patient outcomes.,10 questions used to generate Patient education materials (PEMs),"Duran S, Gonzalez AM, Nguyen K, Nguyen J, Zinn Z. Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures. Pediatr Dermatol. 2024 Nov 12. doi: 10.1111/pde.15805. Epub ahead of print. PMID: 39533849.",Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures,,,2024,Google's Family of LLMs,57
1751,Spanish,Inflesz Scale: Readability level of Patient education materials,"Patient education, Cosmetic dermatology, keloids, scar management and wound healing",93.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,Readability of Spanish PEMs from the Society of Pediatric Dermatology (SPD) was compared with those generated by Open-AI's ChatGPT 4.0 and Google Gemini. AI-generated PEMs could better meet readability standards and potentially improve patient outcomes.,10 questions used to generate Patient education materials (PEMs),"Duran S, Gonzalez AM, Nguyen K, Nguyen J, Zinn Z. Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures. Pediatr Dermatol. 2024 Nov 12. doi: 10.1111/pde.15805. Epub ahead of print. PMID: 39533849.",Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures,,,2024,OpenAI GPT series,57
1752,Spanish,Inflesz Scale: Readability level of Patient education materials,"Patient education, Cosmetic dermatology, keloids, scar management and wound healing",90.00%,10,Gemini 1.0,Gemini 1.0,3/17/2023,Readability of Spanish PEMs from the Society of Pediatric Dermatology (SPD) was compared with those generated by Open-AI's ChatGPT 4.0 and Google Gemini. AI-generated PEMs could better meet readability standards and potentially improve patient outcomes.,10 questions used to generate Patient education materials (PEMs),"Duran S, Gonzalez AM, Nguyen K, Nguyen J, Zinn Z. Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures. Pediatr Dermatol. 2024 Nov 12. doi: 10.1111/pde.15805. Epub ahead of print. PMID: 39533849.",Enhancing Spanish Patient Education Materials: Comparing the Readability of Artificial Intelligence-Generated Spanish Patient Education Materials to the Society of Pediatric Dermatology Spanish Patient Brochures,,,2024,Google's Family of LLMs,57
1753,English,Efficiency of AI in Dermatology Board Exam question generation,Professional education,31.10%,402,ChatGPT 4,ChatGPT-4,3/14/2023,"Validity Score  is based  on the Suitablity measure. It will be slightly lower if the Overall Validity Score could be also computed as the unweighted average of five HealthBench-style dimensions for each subject. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1754,English,Biopsy techniques in dermatology,Professional education,65.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Based on suitability and rejection reasons, tends to be ""too easy"" or ""Errored"" (lower scores for complexity). Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1755,English,CBCL,Professional education,63.30%,30,ChatGPT 4,ChatGPT-4,3/14/2023,"Dispute reasons and suitability indicate moderate scores. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1756,English,CTCL,Professional education,53.30%,30,ChatGPT 4,ChatGPT-4,3/14/2023,"Similar to CBCL, moderate scores. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1757,English,HPV,Professional education,40.60%,32,ChatGPT 4,ChatGPT-4,3/14/2023,"Slightly lower scores, ""Too hard"" questions tend to lower accuracy & completeness. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1758,English,Alopecia,Professional education,40.00%,40,ChatGPT 4,ChatGPT-4,3/14/2023,"Mixed, with lower context awareness. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1759,English,Systemic disease,Professional education,33.30%,30,ChatGPT 4,ChatGPT-4,3/14/2023,"Slightly lower in completeness & accuracy. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1760,English,Mycobacteria,Professional education,26.70%,30,ChatGPT 4,ChatGPT-4,3/14/2023,"Lower in completeness and communication. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1761,English,Darier disease,Professional education,25.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Slightly higher in communication quality. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1762,English,Ichthyoses,Professional education,15.00%,40,ChatGPT 4,ChatGPT-4,3/14/2023,"Very low scores, ""Errored"" questions. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1763,English,Acne,Professional education,15.00%,60,ChatGPT 4,ChatGPT-4,3/14/2023,"Moderate scores, ""Too easy"" questions. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1764,English,Rosacea,Professional education,15.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Slightly higher in context & communication. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1765,English,Vasculitis,Professional education,14.00%,50,ChatGPT 4,ChatGPT-4,3/14/2023,"Very low scores, ""too easy"" but low in accuracy & communication. Validity Score  is based  on the Suitablity measure. It will be slightly lower if computed as the unweighted average of five HealthBench-style dimensions for each subject or average of Accuracy, Context awareness, and Instruction following, most appropriate for assessment.. Completness can be assessed as the “suitability” reflecting whether the answer was seen as fully addressing the topic (percentage of suitable questions). Accuracy combines the two reviewers’ independent judgments of answer correctness: (R1 % + R2 %) / 2; Context awareness = 100 – Dispute % High disagreement implies the model lacks clarity/context ? deductive inverse. Context awareness could be also derived based on rejection reasons like ""too easy,"" ""too hard,"" or ""errored.""; Communication quality = 100 – Dispute % Again, high dispute suggests unclear or ambiguous answers; Instruction following = (Suitable % + R2 %) / 2 Reflects a blend of general suitability and alignment with a reviewer’s expectations.","ChatGPT-4 generated 402 questions, with 208 (51.7%) deemed acceptable by at least 1 reviewer. However, only 72 questions (18%) were accepted by both reviewers. After consensus discussions, 53 of the 136 initially disputed questions were approved, resulting in a total of 125 questions deemed suitable for the exam. The suitable questions were classified as 51 (40.8%) easy, 45 (36%) medium-difficulty, and 29 (23.2%) hard. The main issues with unsuitable questions included questions that contained errors or improperly structured or with potential for an appeal (118 questions, 27.8%) and excessive simplicity (113 questions, 28.1%). By subject area: Biopsy techniques and B-cell lymphoma had the highest rates of suitable questions (63–65%). In addition, 37 questions were 2-stage complicated questions. Of those 7 were determined as appropriate (18.9%). ","Shapiro J, Lyakhovitsky A, Freud T, Pavlotsky F, Khamaysi Z, Valdman-Grinshpoun Y, Dodiuk-Gad R, Goldberg I, Ingber A, Kaplan B, Avitan-Hersh E. Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. Acta Derm Venereol. 2025 Jan 3;105:adv41208. doi: 10.2340/actadv.v105.41208. PMID: 39749389; PMCID: PMC11697136.",Assessing ChatGPT-4's Capabilities in Generating Dermatology Board Examination Content: An Explorational Study. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,62
1766,English,"Melanoma, Clinical note generation",Clinical dermatology,97.03%,33126,ChatGPT 4,ChatGPT-4,3/14/2023,"The experimental results demonstrates a significant improvement in all classification metrics compared to single-modality models, achieving an accuracy of 99.51%, precision of 96.19%, recall of 97.95% and f1-score of 97.03% with the multimodal ALBEF model that uses GPT-4-turbo for clinical notes generation","Clinical notes synthetically generated using two distinct multimodal large language models (LLMs), corresponding to each patient’s skin cancer images, encapsulating both visual and textual data representative of real-world scenarios.: https://www.kaggle.com/competitions/siim-isic-melanoma-classification The generated clinical notes are validated through a dual-method approach involving cross-evaluation and consensus scoring, utilizing metrics such as the BLEU score, ROUGE score, Overlap Coefficient, and Jaccard index. the full Kaggle competition package has ? 44 k images in all; most work focuses on the 33 126-image training set because it comes with diagnostic labels. 584 (?1.8 %) are histopathology-confirmed melanomas; the remainder are benign lesions.","Chakkarapani, V., Poornapushpakala, S. & Suresh, S. Enhancing Skin Cancer Detection with Multimodal Data Integration: A Combined Approach Using Images and Clinical Notes. SN COMPUT. SCI. 6, 72 (2025). https://doi.org/10.1007/s42979-024-03601-x; https://www.kaggle.com/competitions/siim-isic-melanoma-classification",Enhancing Skin Cancer Detection with Multimodal Data Integration: A Combined Approach Using Images and Clinical Notes,,,2025,OpenAI GPT series,63
1767,English,"Image-based diagnosis, Top diagnosis",General dermatological conditions,53.70%,54,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1768,English,"Image-based diagnosis, Top diagnosis",Acne,83.33%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1769,English,"Image-based diagnosis, Top diagnosis",Psoriasis,66.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1770,English,"Image-based diagnosis, Top diagnosis",Basal cell carcinoma (BCC),66.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1771,English,"Image-based diagnosis, Top diagnosis",Eczema,16.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1772,English,"Image-based diagnosis, Top diagnosis",Actinic keratosis (AK),33.33%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1773,English,"Image-based diagnosis, Top diagnosis",Rosacea,100.00%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1774,English,"Image-based diagnosis, Top diagnosis",Squamous cell carcinoma (SCC),16.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1775,English,"Image-based diagnosis, Top diagnosis",Superficial spreading melanoma (SSM),66.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1776,English,"Image-based diagnosis, Top diagnosis",Vitiligo,50.00%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects both accuracy and completness. Rosacea had the highest diagnostic accuracy, where the correct primary diagnosis was provided for 6/6 of the images. Eczema and squamous cell carcinoma had the lowest diagnostic accuracy, where the correct primary diagnosis was provided for 1/6 images within each condition.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1777,English,Image-based differential diagnosis,General dermatological conditions,50.00%,54,ChatGPT 4V,ChatGPT-4V,12/17/2023,Validity reflects accuracy but not completness. ,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1778,English,Image-based differential diagnosis,Acne,33.33%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects accuracy but not completness. In some diseases (e.g., eczema or melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1779,English,Image-based differential diagnosis,Psoriasis,33.33%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,Validity reflects accuracy but not completness. ,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1780,English,Image-based differential diagnosis,Basal cell carcinoma (BCC),83.33%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,Validity reflects accuracy but not completness. ,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1781,English,Image-based differential diagnosis,Eczema,66.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects accuracy but not completness. In some diseases (e.g., eczema or melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1782,English,Image-based differential diagnosis,Actinic keratosis (AK),16.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,Validity reflects accuracy but not completness. ,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1783,English,Image-based differential diagnosis,Rosacea,50.00%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects accuracy but not completness. In some diseases (e.g., eczema or melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1784,English,Image-based differential diagnosis,Squamous cell carcinoma (SCC),66.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,Validity reflects accuracy but not completness. ,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1785,English,Image-based differential diagnosis,Superficial spreading melanoma (SSM),83.33%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,"Validity reflects accuracy but not completness. In some diseases (e.g., eczema or Superficial spreading melanoma), the differential list was clearly stronger; in others (e.g., acne or rosacea), the top guess dominated.",Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1786,English,Image-based differential diagnosis,Vitiligo,16.67%,6,ChatGPT 4V,ChatGPT-4V,12/17/2023,Validity reflects accuracy but not completness. ,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1787,English,"Diagnostic accuracy (text-based), Text-based diagnosis, Top diagnosis, Scenario, Primary Diagnosis, Correct primary diagnosis","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",89.00%,9,ChatGPT 4V,ChatGPT-4V,12/17/2023,Clinical scenarios reviewed by dermatologists,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1788,English,"Diagnostic accuracy (multimodal, image + text). Both image and scenario-based. Top diagnosis. Primary Diagnosis, Correct primary diagnosis","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",89.00%,9,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1789,English,"Image, Primary Diagnosis, Correct primary diagnosis","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",54.00%,54,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1790,English,"Image, Differential Diagnosis, Correct diagnosis identified in differential","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",50.00%,54,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1791,English,"Scenario-based diagnosis, Differential Diagnosis, Correct diagnosis identified in differential","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",56.00%,9,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1792,English,"Scenario-based diagnosis, Treatment Entrustment, Average treatment Entrustment Score","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",78.66%,9,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1793,English,"Multimodal diagnostics, Both (Image-based,  Scenario-based), Differential Diagnosis, Correct diagnosis identified in differential","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",44.00%,9,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1794,English,"Multimodal diagnostics, Both (Image-based,  Scenario-based), Treatment Entrustment, Average treatment Entrustment Score","Acne, psoriasis, basal cell carcinoma (BCC), eczema, actinic keratosis (AK), rosacea, squamous cell carcinoma (SCC), superficial spreading melanoma (SSM), and vitiligo.",81.34%,9,ChatGPT 4V,ChatGPT-4V,12/17/2023,Combination of images and clinical scenarios,Dermnet.nz and Dermatlas.org: 102 images of nine common dermatological conditions were collected from Dermnet.nz and Dermatlas.org.; 54 images were selected and validated by two board-certified dermatologists; 9 clinical text-based scenarios were also created and validated.,"Pillai A, Parappally-Joseph S, Kreutz J, Traboulsi D, Gandhi M, Hardin J. Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study. J Cutan Med Surg. 2025 May 6:12034754251336238. doi: 10.1177/12034754251336238. Epub ahead of print. PMID: 40326457.; Abhinav Pillai, Sharon Parappally Joseph, Jason Kreutz, Danya Trabousi, Maharshi Gandhi, Jori Hardin Evaluating the Diagnostic and Treatment Recommendation Capabilities of GPT-4 Vision in Dermatology medRxiv 2024.01.24.24301743; doi: https://doi.org/10.1101/2024.01.24.24301743",Evaluating the Diagnostic and Treatment Capabilities of GPT-4 Vision in Dermatology: A Pilot Study.,Medical Records and Diagnostic Processes,Clinical Practice,2024,OpenAI GPT series,61
1795,English,Readability of  dermatological patient information leaflets,Acne,0.00%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1796,English,Readability of  dermatological patient information leaflets,Atopic dermatitis,0.00%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1797,English,Readability of  dermatological patient information leaflets,Alopecia,0.00%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1798,English,Readability of  dermatological patient information leaflets,Psoriasis,0.00%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1799,English,Readability of  dermatological patient information leaflets,Rosacea,33.33%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,SMOG: 10.4 (outside 5–8 range); FRET: 50.6 (below 60); FKGLT: 8.4 (within 8–9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility    ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1800,English,Readability of  dermatological patient information leaflets,SCC,33.33%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,SMOG: 11.0 (outside 5–8 range); FRET: 52.3 (below 60); FKGLT: 8.5 (within 8–9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility      ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1801,English,Readability of  dermatological patient information leaflets,BCC,33.33%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,SMOG: 11.1 (outside 5–8 range); FRET: 53.0 (below 60); FKGLT: 8.6 (within 8–9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility     ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1802,English,Readability of  dermatological patient information leaflets,Hidradenitis suppurativa,33.33%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,SMOG: 10.8 (outside 5–8 range); FRET: 48.4 (below 60); FKGLT: 9.0 (within 8–9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1803,English,Readability of  dermatological patient information leaflets,Vitiligo,0.00%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1804,English,Readability of  dermatological patient information leaflets,Melanoma (in situ),0.00%,3,Dr. Dermatology,Dr. Dermatology ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1805,English,Readability of  dermatological patient information leaflets,Acne,0.00%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1806,English,Readability of  dermatological patient information leaflets,Atopic dermatitis,33.33%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,SMOG: 10.1 (outside 5–8 range); FRET: 45.1( below 60); FKGLT: 8.7 (within 8–9). The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets ( existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1807,English,Readability of  dermatological patient information leaflets,Alopecia,0.00%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1808,English,Readability of  dermatological patient information leaflets,Psoriasis,33.33%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1809,English,Readability of  dermatological patient information leaflets,Rosacea,33.33%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1810,English,Readability of  dermatological patient information leaflets,SCC,0.00%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1811,English,Readability of  dermatological patient information leaflets,BCC,33.33%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility    ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1812,English,Readability of  dermatological patient information leaflets,Hidradenitis suppurativa,33.33%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1813,English,Readability of  dermatological patient information leaflets,Vitiligo,0.00%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1814,English,Readability of  dermatological patient information leaflets,Melanoma (in situ),0.00%,3,Dermatology Adviser,Dermatology Adviser ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1815,English,Readability of  dermatological patient information leaflets,Acne,33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1816,English,Readability of  dermatological patient information leaflets,Atopic dermatitis,0.00%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1817,English,Readability of  dermatological patient information leaflets,Alopecia,0.00%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1818,English,Readability of  dermatological patient information leaflets,Psoriasis,33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1819,English,Readability of  dermatological patient information leaflets,Rosacea,33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1820,English,Readability of  dermatological patient information leaflets,SCC,33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1821,English,Readability of  dermatological patient information leaflets,BCC,33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility    ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1822,English,Readability of  dermatological patient information leaflets,Hidradenitis suppurativa,33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1823,English,Readability of  dermatological patient information leaflets,Vitiligo,0.00%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1824,English,Readability of  dermatological patient information leaflets,Melanoma (in situ),33.33%,3,Chat With A Dermatologist,Chat with a Dermatologist ,11/17/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1825,English,Readability of  dermatological patient information leaflets,Acne,33.33%,3,ChatGPT 4,ChatGPT-4,3/14/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1826,English,Readability of  dermatological patient information leaflets,Atopic dermatitis,0.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1827,English,Readability of  dermatological patient information leaflets,Alopecia,0.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1828,English,Readability of  dermatological patient information leaflets,Psoriasis,0.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1829,English,Readability of  dermatological patient information leaflets,Rosacea,33.33%,3,ChatGPT 4,ChatGPT-4,3/14/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1830,English,Readability of  dermatological patient information leaflets,SCC,33.33%,3,ChatGPT 4,ChatGPT-4,3/14/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1831,English,Readability of  dermatological patient information leaflets,BCC,33.33%,3,ChatGPT 4,ChatGPT-4,3/14/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Human generated leaflets ( existing British Association of Dermatologists (BAD) PILs) received the same score; documents regenerated to demonstrate reproducibility    ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1832,English,Readability of  dermatological patient information leaflets,Hidradenitis suppurativa,0.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1833,English,Readability of  dermatological patient information leaflets,Vitiligo,0.00%,3,ChatGPT 4,ChatGPT-4,3/14/2023,"None of the SMOG, FRET, or FKGLT metrics met the recommended UK readability guidelines; neither did British Association of Dermatologists (BAD) PILs.  Ideally, FRET scores should fall between 60 and 70, and FKGLT scores should range between  8 and 9 to meet the recommended readability standards","30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1834,English,Readability of  dermatological patient information leaflets,Melanoma (in situ),33.33%,3,ChatGPT 4,ChatGPT-4,3/14/2023,SMOG: outside 5–8 range; FRET: below 60; FKGLT: within 8–9. The target readability requirements are: SMOG: between 5 and 8; FRET: between 60 and 70; FKGLT: between 8 and 9. Only 1 out of 3 metrics meets the requirement. Compare to 0% for human generated leaflets (existing British Association of Dermatologists (BAD) PILs); documents regenerated to demonstrate reproducibility   ,"30: score computed from SMOG, FRET and FKGLT scores generated for each of 10 documents; documents regenerated to demonstrate reproducibility","Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1835,English,"Aims of the PIL, Orientation/Introduction","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1836,English,"More about the condition, Background Information","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1837,English,"What causes the condition, Etiology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1838,English,"Is the condition hereditary?, Genetic/Inheritance Factors","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",30.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1839,English,"What does the condition look like?, Visual Description","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1840,English,"What does the condition feel like?/Symptoms, Symptomatology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1841,English,"How is the condition diagnosed?, Diagnostic Process","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",80.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1842,English,"Can the condition be cured?, Prognosis","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",40.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1843,English,"What is the treatment?, Treatment Options","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1844,English,"Self-care advice, Patient Empowerment/Self-Help","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1845,English,"Additional information for the patient, Supplementary Resources","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,Dr. Dermatology,Dr. Dermatology,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin Dr. Dermatology: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1846,English,"Aims of the PIL, Orientation/Introduction","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1847,English,"More about the condition, Background Information","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1848,English,"What causes the condition, Etiology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1849,English,"Is the condition hereditary?, Genetic/Inheritance Factors","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",50.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1850,English,"What does the condition look like?, Visual Description","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1851,English,"What does the condition feel like?/Symptoms, Symptomatology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",90.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1852,English,"How is the condition diagnosed?, Diagnostic Process","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",80.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1853,English,"Can the condition be cured?, Prognosis","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",30.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1854,English,"What is the treatment?, Treatment Options","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1855,English,"Self-care advice, Patient Empowerment/Self-Help","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1856,English,"Additional information for the patient, Supplementary Resources","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Dermatology Adviser,Dermatology Adviser,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Dermatology Adviser"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1857,English,"Additional information for the patient, Supplementary Resources","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1858,English,"Aims of the PIL, Orientation/Introduction","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1859,English,"More about the condition, Background Information","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1860,English,"What causes the condition, Etiology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",10.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1861,English,"Is the condition hereditary?, Genetic/Inheritance Factors","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1862,English,"What does the condition look like?, Visual Description","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1863,English,"What does the condition feel like?/Symptoms, Symptomatology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",70.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1864,English,"How is the condition diagnosed?, Diagnostic Process","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",40.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1865,English,"Can the condition be cured?, Prognosis","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1866,English,"What is the treatment?, Treatment Options","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1867,English,"Self-care advice, Patient Empowerment/Self-Help","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1868,English,"Additional information for the patient, Supplementary Resources","Most prevalent dermatological conditions in individuals aged 18 and older: acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,Chat With A Dermatologist,Chat with a Dermatologist,11/17/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT plugin ""Chat with a Dermatologist"": sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1869,English,"Additional information for the patient, Supplementary Resources","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1870,English,"Aims of the PIL, Orientation/Introduction","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1871,English,"More about the condition, Background Information","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1872,English,"What causes the condition, Etiology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",60.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1873,English,"Is the condition hereditary?, Genetic/Inheritance Factors","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1874,English,"What does the condition look like?, Visual Description","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1875,English,"What does the condition feel like?/Symptoms, Symptomatology","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",90.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1876,English,"How is the condition diagnosed?, Diagnostic Process","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",30.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1877,English,"Can the condition be cured?, Prognosis","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1878,English,"What is the treatment?, Treatment Options","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",100.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1879,English,"Self-care advice, Patient Empowerment/Self-Help","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1880,English,"Additional information for the patient, Supplementary Resources","Most prevalent dermatological conditions in individuals aged 18 and older : acne, atopic dermatitis, alopecia, psoriasis, rosacea, squamous cell carcinoma (SCC), basal cell carcinoma (BCC), hidradenitis suppurativa, vitiligo and melanoma. ",0.00%,20,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1881,English,Accuracy in terms of no Misleading information generated,"Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ)",100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1882,English,Accuracy in terms of no Misleading information generated,"Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ)",100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1883,English,Accuracy in terms of no Misleading information generated,"Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ)",100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1884,English,Misleading information generated,"Most prevalent dermatological conditions in individuals aged 18 and older: Acne, Atopic dermatitis, Alopecia, Psoriasis, Rosacea, SCC, BCC, Hidradenitis suppurativa, Vitiligo, Melanoma (in situ)",100.00%,10,ChatGPT 4,ChatGPT-4,3/14/2023,"Comparison of the most common subsections in existing British Association of Dermatology patient information  leaflets (PILs) with content inclusion in patient information leaflets generated by ChatGPT-4: sections on cure and heritability were notably absent from most of the ChatGPT generated PILs, which tend to be of interest to patients. This  highlights the advantage of human-created PILs addressing  the specific needs and questions more effectively.",,"Todorov D, Park JY, Hing Cheung JA, Avramidou E, Gnanappiragasam D. Assessing the readability of dermatological patient information leaflets generated by ChatGPT-4 and its associated plugins. Skin Health and Disease. 2025 Jan 20:vzae015.",,Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,60
1885,English,"Allergic dermatology, accuracy of answers",Fragrance allergy,88.00%,40,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"one critical error: recommended natural fragrances despite allergy. Results for accuracy (ACC) were good with a mean Likert grade of 4.1 on a 5-step scale (SD = 0.78, range = 1-5, IQR:293 3-5) across all 3 specialties (dermatology: mean = 4.4 [range = 4-5, IQR: 4-5]; pediatrics mean = 3.8 [range294 = 1-5, IQR: 3-5]; pulmonology mean = 4.1 [range = 2-5, IQR: 4-5]) corresponding to an overall adequate accuracy of ChatGPT's answers, with no significant distinction. ; The mean completeness (CO) Likert grades (3-grade scale) were consistent across all three specialties (overall mean = 2.32; dermatology mean = 2.38; pediatrics mean = 2.25; pulmonology mean = 2.33).",40 routine-care dermatology allergology questions (out of 120 questions from routine allergological practice),"Mathes S, Seurig S, Bluhme F, Beyer K, Heizmann F, Wagner M, Neugärtner I, Biedermann T, Darsow U. ChatGPT performance on 120 interdisciplinary allergology questions - systematic evaluation with clinical error impact assessment for critical erroneous AI-guided chatbot-advice. J Allergy Clin Immunol Pract. 2025 Mar 27:S2213-2198(25)00280-6. doi: 10.1016/j.jaip.2025.03.030. Epub ahead of print. PMID: 40157421.",ChatGPT performance on 120 interdisciplinary allergology questions,Patient Education Materials and Readability Studies,Healthcare AI,2025,OpenAI GPT series,64
1886,English,"Allergic dermatology, completeness of answers",Fragrance allergy,79.33%,40,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"one critical error: recommended natural fragrances despite allergy. Results for accuracy (ACC) were good with a mean Likert grade of 4.1 on a 5-step scale (SD = 0.78, range = 1-5, IQR:293 3-5) across all 3 specialties (dermatology: mean = 4.4 [range = 4-5, IQR: 4-5]; pediatrics mean = 3.8 [range294 = 1-5, IQR: 3-5]; pulmonology mean = 4.1 [range = 2-5, IQR: 4-5]) corresponding to an overall adequate accuracy of ChatGPT's answers, with no significant distinction. ; The mean completeness (CO) Likert grades (3-grade scale) were consistent across all three specialties (overall mean = 2.32; dermatology mean = 2.38; pediatrics mean = 2.25; pulmonology mean = 2.33).",40 routine-care dermatology allergology questions (out of 120 questions from routine allergological practice),"Mathes S, Seurig S, Bluhme F, Beyer K, Heizmann F, Wagner M, Neugärtner I, Biedermann T, Darsow U. ChatGPT performance on 120 interdisciplinary allergology questions - systematic evaluation with clinical error impact assessment for critical erroneous AI-guided chatbot-advice. J Allergy Clin Immunol Pract. 2025 Mar 27:S2213-2198(25)00280-6. doi: 10.1016/j.jaip.2025.03.030. Epub ahead of print. PMID: 40157421.",ChatGPT performance on 120 interdisciplinary allergology questions,Patient Education Materials and Readability Studies,Healthcare AI,2025,OpenAI GPT series,64
1887,English,"Teledermatology consultations, exact match with the teledermatologist's diagnoses","Top diagnosis, exact match with the teledermatologist's diagnoses",70.80%,154,ChatGPT 4,ChatGPT-4,3/14/2023,"Out of 154 cases, ChatGPT?4 achieved a Top1 diagnostic concordance in 108 (70.8%), Top3 concordance in 137 (87.7%), partial concordance in four (2.6%), and was discordant in 15 (9.7%) cases. The quality of ChatGPT?4's image descriptions significantly surpassed teledermatologists in all five parameters. ChatGPT?4's descriptions were accurate in 130 (84.4%), partially accurate in 22 (14.3%), and inaccurate in two (1.3%) cases.",154 teledermatology consultations (December 2023–February 2024),"Shapiro J, Avitan?Hersh E, Greenfield B, Khamaysi Z, Dodiuk?Gad RP, Valdman?Grinshpoun Y, Freud T, Lyakhovitsky A. The use of a ChatGPT?4?based chatbot in teledermatology: A retrospective exploratory study. JDDG: Journal der Deutschen Dermatologischen Gesellschaft. 2025 Jan 1.",The use of a ChatGPT?4?based chatbot in teledermatology: A retrospective exploratory study. ,Teledermatology Image Assessment,Clinical Practice,2025,OpenAI GPT series,65
1888,English,"Teledermatology consultations, correct differential diagnosis (matches top three teledermatologist's diagnoses)",Differential diagnosis (matches top three teledermatologist's diagnoses),87.70%,154,ChatGPT 4,ChatGPT-4,3/14/2023,"Out of 154 cases, ChatGPT?4 achieved a Top1 diagnostic concordance in 108 (70.8%), Top3 concordance in 137 (87.7%), partial concordance in four (2.6%), and was discordant in 15 (9.7%) cases. The quality of ChatGPT?4's image descriptions significantly surpassed teledermatologists in all five parameters. ChatGPT?4's descriptions were accurate in 130 (84.4%), partially accurate in 22 (14.3%), and inaccurate in two (1.3%) cases.",154 teledermatology consultations (December 2023–February 2024),"Shapiro J, Avitan?Hersh E, Greenfield B, Khamaysi Z, Dodiuk?Gad RP, Valdman?Grinshpoun Y, Freud T, Lyakhovitsky A. The use of a ChatGPT?4?based chatbot in teledermatology: A retrospective exploratory study. JDDG: Journal der Deutschen Dermatologischen Gesellschaft. 2025 Jan 1.",The use of a ChatGPT?4?based chatbot in teledermatology: A retrospective exploratory study. ,Teledermatology Image Assessment,Clinical Practice,2025,OpenAI GPT series,65
1889,English,"Teledermatology consultations, similar but not necessarily identical diagnoses",Similar diagnoses,90.26%,154,ChatGPT 4,ChatGPT-4,3/14/2023,"Out of 154 cases, ChatGPT?4 achieved a Top1 diagnostic concordance in 108 (70.8%), Top3 concordance in 137 (87.7%), partial concordance in four (2.6%), and was discordant in 15 (9.7%) cases. The quality of ChatGPT?4's image descriptions significantly surpassed teledermatologists in all five parameters. ChatGPT?4's descriptions were accurate in 130 (84.4%), partially accurate in 22 (14.3%), and inaccurate in two (1.3%) cases.",154 teledermatology consultations (December 2023–February 2024),"Shapiro J, Avitan?Hersh E, Greenfield B, Khamaysi Z, Dodiuk?Gad RP, Valdman?Grinshpoun Y, Freud T, Lyakhovitsky A. The use of a ChatGPT?4?based chatbot in teledermatology: A retrospective exploratory study. JDDG: Journal der Deutschen Dermatologischen Gesellschaft. 2025 Jan 1.",The use of a ChatGPT?4?based chatbot in teledermatology: A retrospective exploratory study. ,Teledermatology Image Assessment,Clinical Practice,2025,OpenAI GPT series,65
1890,English,Medical Knowledge Assessment,"Dermatology, Venereology, and Leprosy - Multiple Topics",59.80%,60,ChatGPT 3.5,"ChatGPT v3.5, August 2023",11/30/2022,Performed best on hard questions; struggled with inflammatory diseases and leprosy; 4 hallucinations,"Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1891,English,Medical Knowledge Assessment,"Dermatology, Venereology, and Leprosy - Multiple Topics",78.20%,60,Bing,"Bing Chat, August 2023",8/1/2023,Outperformed both ChatGPT and human respondents overall; no hallucinations; best on easy and medium questions,"Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1892,English,Medical Knowledge Assessment,Basic sciences,70.00%,3,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT , Mean ± SD (%) 2.1 ± 0.2 (70%) vs Total correct by human respondents 1.7 ± 0.2 (56.7%), p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1893,English,Medical Knowledge Assessment,Infections,52.50%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT , Mean ± SD (%) 2.1 ± 0.2 (52.5%) vs Total correct by human respondents 2.3 ± 0.4 (57.5%), p value for difference between humans and LLM 0.024","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1894,English,Medical Knowledge Assessment,Inflammatory diseases,12.90%,5,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT , Mean ± SD (%) 0.4 ± 0.2 (12.9%) vs Total correct by human respondents 3.1 ± 0.5 (62%), p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1895,English,Medical Knowledge Assessment,Connective tissue disorders,85.00%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT , Mean ± SD (%) 3.4 ± 0.2 (85%) vs Total correct by human respondents 2.0 ± 0.2 (50%), p value for difference between humans and LLM 0.056","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1896,English,Medical Knowledge Assessment,Genodermatoses,86.70%,6,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT , Mean ± SD (%) 5.2 ± 0.3 (86.7%) vs Total correct by human respondents 3.1 ± 0.7 (51.7%), p value for difference between humans and LLM 0.047","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1897,English,Medical Knowledge Assessment,Metabolic disorders,86.00%,5,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 4.3 ± 0.2 (86%)  vs Total correct by human respondents 3.0 ± 0.3 (60%), p value for difference between humans and LLM 0.061","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1898,English,Medical Knowledge Assessment,Vascular diseases,87.50%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 3.5 ± 0.2 (87.5%)  vs Total correct by human respondents 1.9 ± 0.4 (47.5%), p value for difference between humans and LLM 0.011","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1899,English,Medical Knowledge Assessment,Pilosebaceous disorders,64.00%,5,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 3.2 ± 0.3 (64%)  vs Total correct by human respondents 2.5 ± 0.2 (50%), p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1900,English,Medical Knowledge Assessment,Cutaneous adverse drug reactions,52.50%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 2.1 ± 0.2 (52.5%)  vs Total correct by human respondents 1 ± 0.3 (25%), p value for difference between humans and LLM 0.01","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1901,English,Medical Knowledge Assessment,Benign and malignant tumours,55.00%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 2.2 ± 0.2 (55%)  vs Total correct by human respondents 2.1 ± 0.3 (52.5%), p value for difference between humans and LLM 0.058","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1902,English,Medical Knowledge Assessment,Therapeutics,36.70%,3,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 1.1 ± 0.3 (36.7%)  vs Total correct by human respondents 1.5 ± 0.2 (50%), p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1903,English,Medical Knowledge Assessment,Leprosy,22.00%,5,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 1.1 ± 1.1 (22%)  vs Total correct by human respondents 3.4 ± 0.2 (68%), p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1904,English,Medical Knowledge Assessment,Sexually transmitted diseases,77.50%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 3.1 ± 0.2 (77.5%)  vs Total correct by human respondents 2.1 ± 0.4 (52.5%), p value for difference between humans and LLM 0.023","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1905,English,Medical Knowledge Assessment,Aesthetic dermatology,52.50%,4,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 2.1 ± 0.5 (52.5%)  vs Total correct by human respondents 1.4 ± 0.3 (35%), p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1906,English,Medical Knowledge Assessment,"Dermatology, Venereology, and Leprosy - Multiple Topics",35.90%,60,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"Total correct by ChatGPT, 35.9±0.5  vs Total correct by human respondents 25.8±11.0, p value for difference between humans and LLM <0.001","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1907,English,Medical Knowledge Assessment,Basic sciences,73.30%,3,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 2.2 ± 0.3 (73.3%)  vs Total correct by human respondents 1.7 ± 0.2 (56.7%), p value for difference between humans and LLM ","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1908,English,Medical Knowledge Assessment,Infections,85.00%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.4 ± 0.1 (85%)  vs Total correct by human respondents 2.3 ± 0.4 (57.5%), p value for difference between humans and LLM ","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1909,English,Medical Knowledge Assessment,Inflammatory diseases,44.00%,5,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 2.2 ± 0.7 (44%)  vs Total correct by human respondents 3.1 ± 0.5 (62%), p value for difference between humans and LLM ","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1910,English,Medical Knowledge Assessment,Connective tissue disorders,80.00%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.2 ± 0.3 (80%)  vs Total correct by human respondents 2.0 ± 0.2 (50%), p value for difference between humans and LLM ","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1911,English,Medical Knowledge Assessment,Genodermatoses,86.70%,6,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 5.2 ± 0.3 (86.7%)  vs Total correct by human respondents 3.1 ± 0.7 (51.7%), p value for difference between humans and LLM ","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1912,English,Medical Knowledge Assessment,Metabolic disorders,88.00%,5,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 4.4 ± 0.3 (88%)  vs Total correct by human respondents 3.0 ± 0.3 (60%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1913,English,Medical Knowledge Assessment,Vascular diseases,85.00%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.4 ± 0.4 (85%)  vs Total correct by human respondents 1.9 ± 0.4 (47.5%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1914,English,Medical Knowledge Assessment,Pilosebaceous disorders,84.00%,5,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 4.2 ± 0.3 (84%)  vs Total correct by human respondents 2.5 ± 0.2 (50%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1915,English,Medical Knowledge Assessment,Cutaneous adverse drug reactions,55.00%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 2.2 ± 0.3 (55%)  vs Total correct by human respondents 1 ± 0.3 (25%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1916,English,Medical Knowledge Assessment,Benign and malignant tumours,80.00%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.2 ± 0.2 (80%)  vs Total correct by human respondents 2.1 ± 0.3 (52.5%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1917,English,Medical Knowledge Assessment,Therapeutics,76.70%,3,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 2.3 ± 0.2 (76.7%)  vs Total correct by human respondents 1.5 ± 0.2 (50%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1918,English,Medical Knowledge Assessment,Leprosy,62.00%,5,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.1 ± 0.2 (62%)  vs Total correct by human respondents 3.4 ± 0.2 (68%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1919,English,Medical Knowledge Assessment,Sexually transmitted diseases,80.00%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.2 ± 0.2 (80%)  vs Total correct by human respondents 2.1 ± 0.4 (52.5%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1920,English,Medical Knowledge Assessment,Aesthetic dermatology,77.50%,4,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 3.1 ± 0.3 (77.5%)  vs Total correct by human respondents 1.4 ± 0.3 (35%)","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1921,English,Medical Knowledge Assessment,"Dermatology, Venereology, and Leprosy - Multiple Topics",43.00%,60,Bing,BingChat,8/17/2023,"Total correct by Bing Chat, 46.9±0.7  vs Total correct by human respondents 25.8±11.0","Custom 60-question test covering Dermatology, Venereology, and Leprosy (DVL)","Murthy AB, Palaniappan V, Radhakrishnan S, Rajaa S, Karthikeyan K. A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology. Indian Dermatol Online J. 2025 Feb 27;16(2):241-247. doi: 10.4103/idoj.idoj_221_24. PMID: 40125046; PMCID: PMC11927985.",A Comparative Analysis of the Performance of Large Language Models and Human Respondents in Dermatology,Performance comparison,Professional Education,2025,OpenAI GPT series,67
1922,English,General dermatology questions,"Dermatopathology, Immunodermatology, inflammatory, infectious, and systemic skin diseases",100.00%,285,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Validity is assumed 100% since overall results were satisfactory for both ChatGPT and DermGPT. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. ","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1923,English,General dermatology questions,Psoriasis,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What is the best treatment for mild psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. ","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1924,English,General dermatology questions,Dermatopathology,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What is the difference between a papule and a macule? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1925,English,General dermatology questions,Immunodermatology,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What are the most reported side effects of IL-23 inhibitors? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1926,English,General dermatology questions,Granulomatous Diseases,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What is the etiology of Granuloma Annulare? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1927,English,General dermatology questions,Papulosquamous Disorders,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What dermatoses should be included in the differential diagnosis of a papulosquamous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1928,English,General dermatology questions,Autoimmune Blistering Diseases,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Bullous Pemphigoid is caused by an autoantibody response directed against which antigens? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1929,English,General dermatology questions,Acne,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What are the guidelines for prescribing isotretinoin? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1930,English,General dermatology questions,Vascular Dermatology,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What is the classic clinical appearance of calciphylaxis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1931,English,General dermatology questions,Hidradenitis Suppurativa,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"How do you rate the severity of Hidradenitis Suppurativa? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1932,English,General dermatology questions,Pigmented Lesions/Melanoma,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What margin should be used for lentigo maligna (or severely dysplastic nevus)? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1933,English,General dermatology questions,Psoriasis,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Which is the best biologic for psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1934,English,General dermatology questions,Skin Cancer,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What is the skin cancer screening recommendation for the general population? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1935,English,General dermatology questions,Dermatopathology,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What dermatoses should be included in the differential diagnosis of an eczematous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1936,English,General dermatology questions,Acne,100.00%,19,ChatGPT 4o,ChatGPT-4o,5/13/2024,"What medications are used to treat hormonally exacerbated acne? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1937,English,General dermatology questions,"Dermatopathology, Immunodermatology, inflammatory, infectious, and systemic skin diseases",100.00%,285,Dermgpt,DermGPT,11/17/2023,"Validity is assumed 100% since overall results were satisfactory for both ChatGPT and DermGPT. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. ","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1938,English,General dermatology questions,Psoriasis,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What is the best treatment for mild psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. ","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1939,English,General dermatology questions,Dermatopathology,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What is the difference between a papule and a macule? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1940,English,General dermatology questions,Immunodermatology,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What are the most reported side effects of IL-23 inhibitors? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1941,English,General dermatology questions,Granulomatous Diseases,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What is the etiology of Granuloma Annulare? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1942,English,General dermatology questions,Papulosquamous Disorders,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What dermatoses should be included in the differential diagnosis of a papulosquamous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1943,English,General dermatology questions,Autoimmune Blistering Diseases,100.00%,19,Dermgpt,DermGPT,11/17/2023,"Bullous Pemphigoid is caused by an autoantibody response directed against which antigens? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1944,English,General dermatology questions,Acne,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What are the guidelines for prescribing isotretinoin? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1945,English,General dermatology questions,Vascular Dermatology,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What is the classic clinical appearance of calciphylaxis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1946,English,General dermatology questions,Hidradenitis Suppurativa,100.00%,19,Dermgpt,DermGPT,11/17/2023,"How do you rate the severity of Hidradenitis Suppurativa? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1947,English,General dermatology questions,Pigmented Lesions/Melanoma,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What margin should be used for lentigo maligna (or severely dysplastic nevus)? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1948,English,General dermatology questions,Psoriasis,100.00%,19,Dermgpt,DermGPT,11/17/2023,"Which is the best biologic for psoriasis? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1949,English,General dermatology questions,Skin Cancer,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What is the skin cancer screening recommendation for the general population? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1950,English,General dermatology questions,Dermatopathology,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What dermatoses should be included in the differential diagnosis of an eczematous reaction pattern? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1951,English,General dermatology questions,Acne,100.00%,19,Dermgpt,DermGPT,11/17/2023,"What medications are used to treat hormonally exacerbated acne? (note that only overall results were reported, based on all 15 questions and comparison of two GPTs; overall results were satisfactory for both, hence validity is assumed 100%. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.)","285: A series of 15 dermatology-specific questions were presented to ChatGPT 4o and DermGPT (based on ChatGPT), with the responses subsequently evaluated by a cohort of 19+ practicing dermatologists (attendings, residents and fellows) across two institutions. DermGPT responses were favored overall (48%) compared to ChatGPT (28%), with a statistically significant chi-square test result (p=0.039). In subgroup analysis, attendings preferred DermGPT (47%) over ChatGPT (28%), while residents showed a narrower preference margin (32% vs. 31%). For source preference, ChatGPT citations were favored (46%) over DermGPT (24%), though the chi-square test did not reach statistical significance (p=1.385). Attendings preferred ChatGPT sources (48%) over DermGPT (23%), while residents also favored ChatGPT (41%) over DermGPT (24%). ChatGPT’s citation quality was preferred, whereas DermGPT’s concise style made it more accessible for quick clinical reference. These findings suggest that integrating the strengths of both models could optimize AI-assisted medical consultations, balancing clarity with academic rigor.","Patel AB, Driscoll W, Lee CH, Zachary C, Golbari NM, Smith J Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis JMIR Preprints. 16/03/2025:74040  DOI: 10.2196/preprints.74040  URL: https://preprints.jmir.org/preprint/74040",Evaluating Artificial Intelligence Models in Dermatology: A Comparative Analysis,Dermatology Examinations and Practice Questions; Medical Records & Diagnostic Processes,Professional Education,2025,OpenAI GPT series,69
1952,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pilar (trichilemmal) cyst,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 54-year-old male patient presented with a fixed nodule on the head. The lesion was excised. Histol...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1953,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pilomatricoma with atypical features,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 46-year-old female patient presented with a right periauricular hard lesion that enlarged over the...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1954,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Fibroepithelial polyp,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 68-year-old male patient presented with a chronic anal fissure and a perianal raised skin lesion. ...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1955,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Epidermal inclusion cyst with overlying lentigo simplex,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 55-year-old male patient presented with a left infraorbital papillomatous growth for 2 years that ...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1956,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Basal cell carcinoma, infiltrating type",100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 61-year-old female patient presented with a lesion on the nose for one year with slight bleeding. ...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1957,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Common melanocytic nevus, dermal type",0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 92-year-old female patient presented with a left cheek mass. The lesion was excised. Histology sho...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1958,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Chronic furuncle,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 23-year-old male patient presented with a painful swelling in the right inguinal region. The lesio...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1959,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Low-grade fibrohistiocytic proliferation, consistent with a xanthogranuloma",50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 34-year-old male patient presented with a painless swelling on the scalp for two months with chron...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1960,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Molluscum contagiosum,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 31-year-old female patient presented with two right breast dermal nodules with localized skin thic...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1961,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Inflamed pilonidal sinus tract,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 26-year-old male patient presented with a lower back skin lesion. The lesion was excised. Histolog...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1962,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Hidradenitis suppurativa,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 22-year-old male patient presented with a skin lesion in the axilla. The lesion was excised. Histo...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1963,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Cheek lesion: Seborrheic keratosis, pigmented type; Nose lesion: Common melanocytic nevus, intradermal type",100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Agreement: Complete agreement. Description: A 54-year-old female patient presented with a right cheek skin lesion and a left nose skin lesion, b...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1964,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Molluscum contagiosum,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 43-year-old male patient presented with a lesion on the skin of the right side of the penis. The l...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1965,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lobular capillary hemangioma,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 77-year-old female patient presented with a thumb mass and a history of trauma. The lesion was exc...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1966,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Granulomatous dermatitis; needs further work-up for etiology,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 54-year-old female patient presented with a right nose lesion that was excised. Histology shows sl...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1967,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pemphigus vulgaris,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 40-year-old female patient presented with an oral ulcer. A biopsy shows a fragment of skin with ul...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1968,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Keratosis pilaris,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 36-year-old female patient presented with numerous brown macules at the site of keratotic follicle...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1969,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous lymphoid hyperplasia (pseudolymphoma),100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 70-year-old female patient presented with a left auricular skin lesion that was excised. Histology...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1970,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Metastatic squamous cell carcinoma, well differentiated",100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: An 80-year-old male patient presented with a painful skin lesion over the nose for one month with hi...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1971,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Fibroepithelial polyp,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Agreement: None agreement. Description: A 33-year-old male patient presented with a cheek skin lesion that was excised. Histologically, the ...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1972,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Basal cell carcinoma, nodular pigmented type",0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 71-year-old female patient presented with a small skin lesion on the left side of the nose for one...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1973,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Common melanocytic nevus, compound type",0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 12-year-old male patient presented with a right eyelid lesion that was excised. Histology shows sl...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1974,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Chronic spongiotic dermatitis; clinical correlation for determining subtype,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 36-year-old male patient presented with itchy skin lesions on the trunk and extremities (mostly th...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1975,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",External hemorrhoids,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 34-year-old male patient presented with a perianal lesion that was excised. Histology shows multip...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1976,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",High-grade squamous dysplasia (at least squamous cell carcinoma in situ),100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: An 85-year-old female patient presented with a crusted lesion on the helix of the right ear. A biops...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1977,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Squamous cell carcinoma in-situ (Bowen disease),50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 63-year-old male patient presented with a left facial skin lesion that was excised. Histologically...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1978,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Well-differentiated squamoproliferative lesion, consistent with inverted follicular keratosis; well-differentiated follicular squamous cell carcinoma in situ is a differential",0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 68-year-old male patient presented with a left temporal skin lesion that was excised. Histological...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1979,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Seborrheic keratosis, reticulated (adenoid) type",50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 63-year-old female patient presented with an infra-orbital skin lesion that was excised. Histology...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1980,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Granulomatous septolobular panniculitis, consistent with erythema induratum; requires exclusion of tuberculosis and other causes",50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 67-year-old female patient presented with multiple painless non-itchy erythematous to violaceous s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1981,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Leukocytoclastic vasculitis,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 52-year-old male patient presented with painful annular lesions on both legs for one year which be...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1982,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous lupus erythematosus,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 35-year-old male patient presented with an erythematous skin lesion for one year associated with s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1983,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Guttate psoriasis,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 39-year-old female patient presented with generalized recurrent multiple pink scaly lesions for 9 ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1984,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Actinic keratosis, Bowenoid type",50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 54-year-old female patient presented with a red/brown atrophic 1.2-cm patch on the left cheek for ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1985,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Early lichen sclerosus,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 30-year-old female patient presented with a white patch on the labia majora for 3 months. A biopsy...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1986,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Malignant proliferating trichilemmal tumor (squamous cell carcinoma, moderately differentiated)",50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 59-year-old male patient presented with a left posterior scalp mass that was excised. Histological...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1987,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Inverse psoriasis,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 37-year-old female patient presented with chronic perianal and vaginal pain and pruritus. A biopsy...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1988,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Accessory nipple adenoma,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 23-year-old female patient presented with bilateral accessory breasts and nipples. A biopsy was ta...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1989,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Atypical vascular lesion,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 43-year-old female patient presented with a history of left breast cancer for which she underwent ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1990,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Intravascular papillary endothelial hyperplasia (Masson tumor),0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Agreement: None agreement. Description: A 55-year-old female patient presented with a right infraorbital cystic lesion, clinically suspiciou...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1991,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lichen planus pigmentosus (overlaps with ashy dermatosis),100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 60-year-old female diabetic patient presented with a 1-year history of macular hyperpigmentation s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1992,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous fibrous histiocytoma (dermatofibroma),100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 66-year-old female patient presented with a nodule in the left calf for 5 years without pain or it...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1993,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pancreatic panniculitis,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 66-year-old female patient presented with multiple subcutaneous nodules on the buttock for 4 years...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1994,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Acral lentiginous melanoma in situ,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 38-year-old male patient presented with chronic painless discoloration at the tip of right little ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1995,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pustular psoriasis,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 53-year-old female patient presented with a maculopapular and pustular rash over the upper back bi...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1996,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Actinic keratosis, Bowenoid type",100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 29-year-old male patient with albinism has numerous skin lesions. Excisional biopsy of one lesion ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1997,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lichen planopilaris,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Agreement: Complete agreement. Description: A 17-year-old male patient presented with erythematous, scaly plaques with central scarring over the...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1998,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Nevus sebaceus with basaloid follicular hamartoma,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 25-year-old male patient underwent wide local excision of a skin lesion. Histology shows acanthosi...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
1999,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Dermatofibrosarcoma protuberans,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 23-year-old female patient has a skin lesion excised. Histology shows an ill-defined proliferation...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2000,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Morphea,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 45-year-old male patient presented with gradual hardening of the skin of both legs and thighs for ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2001,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Seborrheic keratosis, irritated type",50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 57-year-old male patient presented with a forehead skin lesion that was excised. Histology shows s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2002,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Focal acantholytic dyskeratosis; differentials include Darier disease and others,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 68-year-old male patient presented with a right inguinal skin lesion. Excision biopsy shows focal ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2003,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous xanthogranuloma,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 31-year-old male patient presented with an asymptomatic papule on the right temple for three month...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2004,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Rosacea,50.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Partial agreement. Description: A 75-year-old male patient presented with a right cheek skin lesion. Excisional biopsy shows multipl...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2005,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Prurigo nodularis,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 37-year-old female patient presented with a recurrent painless lesion under the chin. Clinical dif...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2006,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Prurigo nodularis,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 47-year-old male patient who had a history of diabetes mellitus for 7 years and was on regular tre...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2007,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Kaposi sarcoma, nodular stage",100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 42-year-old male patient presented with a skin lesion on the ventral surface of the glans penis. A...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2008,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Late-stage follicular lichen planus (lichen planopilaris),100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"Agreement: Complete agreement. Description: A 67-year-old male patient presented with a red, itchy, atrophic patch on the scalp with adherent sc...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2009,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pityriasis rosea,0.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: None agreement. Description: A 71-year-old male patient presented with diffuse erythematous and eczematous patches. An incisional...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2010,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lichen planus,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 34-year-old female patient presented with multiple very itchy papules that developed 8 months ago ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2011,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Necrobiosis lipoidica,100.00%,1,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,Agreement: Complete agreement. Description: A 13-year-old female patient with type 1 diabetes mellitus presented with an itchy lesion on the lef...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2012,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas,48.40%,60,ChatGPT 3.5,"ChatGPT-3.5, March 2023",3/1/2023,ChatGPT showed moderate complete agreement. 48.4% vs 60% for experienced pathologists,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2013,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas,71.70%,60,ChatGPT 3.5,"ChatGPT-3.5, March 2023",3/1/2023,Partial or Complete Agreement. Some interpretative discrepancies existed. Similar to 28% for experienced pathologists. Approximately 1 in 4 cases showed no match with the external pathologist,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,70
2014,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pilar (trichilemmal) cyst,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 54-year-old male patient presented with a fixed nodule on the head. The lesion was excised. Histol...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2015,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pilomatricoma with atypical features,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 46-year-old female patient presented with a right periauricular hard lesion that enlarged over the...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2016,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Fibroepithelial polyp,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 68-year-old male patient presented with a chronic anal fissure and a perianal raised skin lesion. ...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2017,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Epidermal inclusion cyst with overlying lentigo simplex,50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 55-year-old male patient presented with a left infraorbital papillomatous growth for 2 years that ...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2018,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Basal cell carcinoma, infiltrating type",100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 61-year-old female patient presented with a lesion on the nose for one year with slight bleeding. ...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2019,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Common melanocytic nevus, dermal type",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 92-year-old female patient presented with a left cheek mass. The lesion was excised. Histology sho...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2020,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Chronic furuncle,50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 23-year-old male patient presented with a painful swelling in the right inguinal region. The lesio...,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2021,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Low-grade fibrohistiocytic proliferation, consistent with a xanthogranuloma",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 34-year-old male patient presented with a painless swelling on the scalp for two months with chron...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2022,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Molluscum contagiosum,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 31-year-old female patient presented with two right breast dermal nodules with localized skin thic...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2023,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Inflamed pilonidal sinus tract,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 26-year-old male patient presented with a lower back skin lesion. The lesion was excised. Histolog...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2024,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Hidradenitis suppurativa,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 22-year-old male patient presented with a skin lesion in the axilla. The lesion was excised. Histo...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2025,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Cheek lesion: Seborrheic keratosis, pigmented type; Nose lesion: Common melanocytic nevus, intradermal type",50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,"Agreement: Partial agreement. Description: A 54-year-old female patient presented with a right cheek skin lesion and a left nose skin lesion, b...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2026,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Molluscum contagiosum,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 43-year-old male patient presented with a lesion on the skin of the right side of the penis. The l...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2027,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lobular capillary hemangioma,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 77-year-old female patient presented with a thumb mass and a history of trauma. The lesion was exc...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2028,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Granulomatous dermatitis; needs further work-up for etiology,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 54-year-old female patient presented with a right nose lesion that was excised. Histology shows sl...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2029,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pemphigus vulgaris,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 40-year-old female patient presented with an oral ulcer. A biopsy shows a fragment of skin with ul...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2030,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Keratosis pilaris,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 36-year-old female patient presented with numerous brown macules at the site of keratotic follicle...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2031,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous lymphoid hyperplasia (pseudolymphoma),0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 70-year-old female patient presented with a left auricular skin lesion that was excised. Histology...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2032,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Metastatic squamous cell carcinoma, well differentiated",100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: An 80-year-old male patient presented with a painful skin lesion over the nose for one month with hi...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2033,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Fibroepithelial polyp,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,"Agreement: None agreement. Description: A 33-year-old male patient presented with a cheek skin lesion that was excised. Histologically, the ...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2034,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Basal cell carcinoma, nodular pigmented type",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 71-year-old female patient presented with a small skin lesion on the left side of the nose for one...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2035,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Common melanocytic nevus, compound type",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 12-year-old male patient presented with a right eyelid lesion that was excised. Histology shows sl...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2036,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Chronic spongiotic dermatitis; clinical correlation for determining subtype,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 36-year-old male patient presented with itchy skin lesions on the trunk and extremities (mostly th...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2037,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",External hemorrhoids,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 34-year-old male patient presented with a perianal lesion that was excised. Histology shows multip...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2038,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",High-grade squamous dysplasia (at least squamous cell carcinoma in situ),100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: An 85-year-old female patient presented with a crusted lesion on the helix of the right ear. A biops...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2039,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Squamous cell carcinoma in-situ (Bowen disease),100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 63-year-old male patient presented with a left facial skin lesion that was excised. Histologically...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2040,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Well-differentiated squamoproliferative lesion, consistent with inverted follicular keratosis; well-differentiated follicular squamous cell carcinoma in situ is a differential",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 68-year-old male patient presented with a left temporal skin lesion that was excised. Histological...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2041,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Seborrheic keratosis, reticulated (adenoid) type",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 63-year-old female patient presented with an infra-orbital skin lesion that was excised. Histology...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2042,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Granulomatous septolobular panniculitis, consistent with erythema induratum; requires exclusion of tuberculosis and other causes",50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 67-year-old female patient presented with multiple painless non-itchy erythematous to violaceous s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2043,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Leukocytoclastic vasculitis,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 52-year-old male patient presented with painful annular lesions on both legs for one year which be...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2044,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous lupus erythematosus,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 35-year-old male patient presented with an erythematous skin lesion for one year associated with s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2045,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Guttate psoriasis,50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 39-year-old female patient presented with generalized recurrent multiple pink scaly lesions for 9 ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2046,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Actinic keratosis, Bowenoid type",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 54-year-old female patient presented with a red/brown atrophic 1.2-cm patch on the left cheek for ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2047,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Early lichen sclerosus,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 30-year-old female patient presented with a white patch on the labia majora for 3 months. A biopsy...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2048,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Malignant proliferating trichilemmal tumor (squamous cell carcinoma, moderately differentiated)",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 59-year-old male patient presented with a left posterior scalp mass that was excised. Histological...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2049,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Inverse psoriasis,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 37-year-old female patient presented with chronic perianal and vaginal pain and pruritus. A biopsy...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2050,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Accessory nipple adenoma,50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 23-year-old female patient presented with bilateral accessory breasts and nipples. A biopsy was ta...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2051,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Atypical vascular lesion,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 43-year-old female patient presented with a history of left breast cancer for which she underwent ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2052,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Intravascular papillary endothelial hyperplasia (Masson tumor),0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,"Agreement: None agreement. Description: A 55-year-old female patient presented with a right infraorbital cystic lesion, clinically suspiciou...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2053,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lichen planus pigmentosus (overlaps with ashy dermatosis),0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 60-year-old female diabetic patient presented with a 1-year history of macular hyperpigmentation s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2054,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous fibrous histiocytoma (dermatofibroma),100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 66-year-old female patient presented with a nodule in the left calf for 5 years without pain or it...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2055,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pancreatic panniculitis,50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 66-year-old female patient presented with multiple subcutaneous nodules on the buttock for 4 years...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2056,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Acral lentiginous melanoma in situ,50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 38-year-old male patient presented with chronic painless discoloration at the tip of right little ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2057,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pustular psoriasis,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 53-year-old female patient presented with a maculopapular and pustular rash over the upper back bi...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2058,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Actinic keratosis, Bowenoid type",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 29-year-old male patient with albinism has numerous skin lesions. Excisional biopsy of one lesion ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2059,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lichen planopilaris,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,"Agreement: None agreement. Description: A 17-year-old male patient presented with erythematous, scaly plaques with central scarring over the...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2060,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Nevus sebaceus with basaloid follicular hamartoma,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 25-year-old male patient underwent wide local excision of a skin lesion. Histology shows acanthosi...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2061,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Dermatofibrosarcoma protuberans,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 23-year-old female patient has a skin lesion excised. Histology shows an ill-defined proliferation...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2062,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Morphea,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 45-year-old male patient presented with gradual hardening of the skin of both legs and thighs for ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2063,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Seborrheic keratosis, irritated type",50.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Partial agreement. Description: A 57-year-old male patient presented with a forehead skin lesion that was excised. Histology shows s...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2064,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Focal acantholytic dyskeratosis; differentials include Darier disease and others,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 68-year-old male patient presented with a right inguinal skin lesion. Excision biopsy shows focal ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2065,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Cutaneous xanthogranuloma,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 31-year-old male patient presented with an asymptomatic papule on the right temple for three month...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2066,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Rosacea,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 75-year-old male patient presented with a right cheek skin lesion. Excisional biopsy shows multipl...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2067,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Prurigo nodularis,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 37-year-old female patient presented with a recurrent painless lesion under the chin. Clinical dif...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2068,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Prurigo nodularis,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 47-year-old male patient who had a history of diabetes mellitus for 7 years and was on regular tre...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2069,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist","Kaposi sarcoma, nodular stage",0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 42-year-old male patient presented with a skin lesion on the ventral surface of the glans penis. A...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2070,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Late-stage follicular lichen planus (lichen planopilaris),0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,"Agreement: None agreement. Description: A 67-year-old male patient presented with a red, itchy, atrophic patch on the scalp with adherent sc...",,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2071,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Pityriasis rosea,0.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: None agreement. Description: A 71-year-old male patient presented with diffuse erythematous and eczematous patches. An incisional...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2072,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Lichen planus,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 34-year-old female patient presented with multiple very itchy papules that developed 8 months ago ...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2073,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Necrobiosis lipoidica,100.00%,1,Gemini 1.0,Gemini 1.0,12/15/2023,Agreement: Complete agreement. Description: A 13-year-old female patient with type 1 diabetes mellitus presented with an itchy lesion on the lef...,,"Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2074,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas,33.00%,60,Gemini 1.0,"Gemini, March 2023",3/1/2023,Gemini showed 33% complete agreement vs 60% for experienced pathologists. Lower performance in complete agreement compared to ChatGPT. ,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2075,English,"Diagnostics from histopathologic report texts, Agreement with external pathologist",Diverse set of dermatology diagnoses not easily classified into a single pathology type like cysts or carcinomas,48.00%,60,Gemini 1.0,"Gemini, March 2023",3/1/2023,Significantly fewer partial agreements. Compare to 28% for experienced pathologists. 33% complete agreement vs 60% for experienced pathologists Over half of Gemini’s responses did not align with the external pathologist,"60 real case scenarios, with half being neoplastic conditions and the other half non-neoplastic, selected by a pathologist from a hospital’s medical database. The cases involved patients who had undergone biopsy and histopathological examination for skin conditions.","Talar Sabir Ahmed, Rawa M. Ali, Ari M. Abdullah, Hadeel A. Yasseen, Ronak S. Ahmed, Ameer M. Salih, et al. Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study. Barw Medical Journal. 2025 Apr. 30;3(3):6-12. doi: https://doi.org/10.58742/bmj.v3i3.180  ",Exploring Large Language Models Integration in the Histopathologic Diagnosis of Skin Diseases: A Comparative Study,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,70
2076,English,Accuracy of LLM-generated handouts,Eczema,81.33%,6,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2077,English,Completeness of LLM-generated handouts,Eczema,85.33%,6,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2078,English,Accuracy of LLM-generated handouts,Eczema,82.33%,6,ChatGPT 4,ChatGPT-4,3/14/2023,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2079,English,Completeness of LLM-generated handouts,Eczema,85.67%,6,ChatGPT 4,ChatGPT-4,3/14/2023,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2080,English,Accuracy of LLM-generated handouts,Juvenile Xanthogranuloma (JXG),85.33%,4,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2081,English,Completeness of LLM-generated handouts,Juvenile Xanthogranuloma (JXG),89.67%,4,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2082,English,Accuracy of LLM-generated handouts,Juvenile Xanthogranuloma (JXG),93.67%,4,ChatGPT 4,ChatGPT-4,3/14/2023,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2083,English,Completeness of LLM-generated handouts,Juvenile Xanthogranuloma (JXG),100.00%,4,ChatGPT 4,ChatGPT-4,3/14/2023,"LLM-generated Handouts were evaluated by pediatric dermatologists using 3-point scale. Five pediatric dermatology conditions were selected based on prevalence and complexity. 6 pediatric dermatologists reviewed handouts for Eczema and 4 for Juvenile Xanthogranuloma (JXG). For Eczema, ChatGPT 3.5 scored 2.44 (accuracy) and 2.56 (completeness), while ChatGPT 4 scored 2.47 (accuracy) and 2.57 (completeness). For JXG, ChatGPT 3.5 scored 2.56 (accuracy) and 2.69 (completeness), while ChatGPT 4 scored 2.81 (accuracy) and 3.00 (completeness). Validity scores are based on a 3-point scale, converted to percentages: Complete (3/3) = 100% Partial (2/3) ? 66.67%  Minimal (1/3) ? 33.33%",,"Needle, Carli et al. 66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics Journal of Investigative Dermatology, 2025, Volume 145, Issue 3, e17","66: Assessing Accuracy, Completeness, and Readability of ChatGPT-Generated Handouts on Pediatric Dermatology Topics",Patient Education Materials and Readability Studies,Patient Education,2025,OpenAI GPT series,68
2084,English,"CognetMD benchmark questions, Muiltiple-choice (penalized for erroneous choices)",General Dermatology,55.00%,542,ChatGPT 4,ChatGPT-4,3/14/2023,"The authors found that GPT-4 performed well regarding questions with only one correct answer, as it achieved full credit for 64/75 questions in Psychiatry, 26/36 in Pulmonology and 53/76 in Dentistry. However, when penalized for erroneous choices, GPT-4 scored averagely, achieving only a 55% accuracy",542 Medical domain-specific questions (MCQs) including 144 dermatology questions,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,71
2085,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),100.00%,1,ChatGPT o3,ChatGPT o3 5-28-2025,5/1/2025,"Correct responses: A, B, E.  A: Hodgkin’s disease Generalised (sometimes intense) pruritus is a well-recognised paraneoplastic symptom and may precede other signs of the lymphoma;  B Pityriasis rosea Around half of patients report troublesome itch; because the secondary eruption is widespread over the trunk and limbs, the pruritus is often perceived as generalised;  E Polycythaemia rubra vera Aquagenic or spontaneous generalised itching affects up to 70 % of patients with this myeloproliferative disorder.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2086,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),83.33%,1,ChatGPT o3,ChatGPT o3 4-16-2025,5/1/2025,A-E vs key A-B-E: Correct answers: A. Hodgkin’s disease E. Polycythaemia rubra vera. B: Pityriasis rosea may be mildly itchy within the lesions but isn’t a systemic cause of generalised pruritus.,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2087,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),66.67%,1,Command,command-a-03-2025,7/1/2025,"A-D-E vs key A-B-E: The correct responses that may include **generalised pruritus** as a symptom are: **A. Hodgkins disease** , **D. Haemolytic jaundice** , **E. Polycythaemia rubra vera**- **Hodgkins disease (A):** Generalised pruritus is a well-known symptom in Hodgkins lymphoma, often associated with the release of cytokines and other inflammatory mediators.  - **Haemolytic jaundice (D):** Pruritus is a common symptom in haemolytic conditions due to the accumulation of bile salts in the skin.  - **Polycythaemia rubra vera (E):** Pruritus, especially after warm water exposure, is a classic symptom of polycythaemia rubra vera, likely due to increased histamine release.  **B. Pityriasis rosea (B)** and **C. Diabetes mellitus (C)** are not typically associated with generalised pruritus as a primary symptom. Pityriasis rosea may cause localized itching, and diabetes can cause pruritus in specific areas (e.g., due to dry skin or fungal infections), but not generalized pruritus as a hallmark symptom.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2088,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),83.33%,1,Gemini 2.5,Gemini 2.5 Flash,5/1/2025,"All of the listed conditions (A. Hodgkin's disease, B. Pityriasis rosea, C. Diabetes mellitus, D. Haemolytic jaundice, E. Polycythaemia rubra vera) can include generalized pruritus as a symptom.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2089,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),66.67%,1,Claude 4,claude-opus-4-20250514,5/1/2025,"key was A-B-E. Claude's answer was: A, C, D, E  The key you provided (A-B-E) appears to be incorrect, as it excludes diabetes mellitus and hemolytic jaundice (both well-established causes of generalized pruritus) while including pityriasis rosea (which typically doesn't cause generalized pruritus).",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Claude series,88
2090,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),66.67%,1,Gemini 2.5,Gemini 2.5 Pro,5/1/2025,key was A-B-E. Answer was A-C-E A. Hodgkin's disease C. Diabetes mellitus E. Polycythaemia rubra vera,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2091,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),66.67%,1,Mistral,Mistral-small-2506,5/1/2025,"key was A-B-E. All of the listed conditions (A. Hodgkin's disease, B. Pityriasis rosea, C. Diabetes mellitus, D. Haemolytic jaundice, E. Polycythaemia rubra vera) can include generalized pruritus as a symptom.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Mistral series,88
2092,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),70.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,key was A-B-E.  Model's answer:A. Hodgkin's disease; D. Haemolytic jaundice; E. Polycythaemia rubra vera,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2093,English,"CognetMD benchmark questions, Muiltiple-choice",On the Following Dermatology Multiple choice Quiz choose correct response or responses. This is an example question: Which condition may include generalised pruritus as a symptom? A. Hodgkins disease B. Pityriasis rosea C. Diabetes mellitus D. Haemolytic jaundice E. Polycythaemia rubra vera   Key: A-B-E Topic: Itchy skin (pruritus),70.00%,1,Unkn,folsom-07172025-1,7/1/2025,key was A-B-E.  Model's answer: A. Hodgkins disease and E. Polycythaemia rubra vera.,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2094,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar’s paste application around the ulcer,33.33%,1,Gemini 2.5,Gemini 2.5 Flash,5/1/2025,"CognetMD suggests A-C-E as correct choices. In early 2025, Gemini 2.5picked A as the only answer. Gemini's comments about C and E: C: While it can kill a broad range of microorganisms, its routine use on chronic ulcers is controversial. E:  While it might be beneficial for the surrounding eczematous skin by providing a barrier and soothing properties, it's generally applied around the ulcer and not directly into the wound itself. It can be a useful adjunct for managing the eczema. Would be rated as 1/3=33% according to the key, but should be 100% according to U guidelines",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2095,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar’s paste application around the ulcer,100.00%,1,ChatGPT o3,ChatGPT-o3,5/1/2025,"CognetMD suggests A-C-E as correct choices. O3 explains regional variations related to drug branding/availability, local reimbursement, and, the integration of traditional regional medicine dressings alongside standard care. A is standard care. *Brand name “Betnovate?C” is discontinued in the UK/US but the generic betamethasone?+?clioquinol combination or separate agents can be prescribed; clinicians use generic betamethasone?0.1?% or medium?potency triamcinolone 0.1?%. Combination steroid?+?antimicrobial creams are discouraged because of antimicrobial?stewardship rule; Modern wound?care protocols favor cadexomer?iodine or poly?hexanide over povidone?iodine; PI is used mainly when bacterial load is high and for <?7?days PI ointment or aqueous solution is still common; practice varies by hospital PI remains a first?line inexpensive antiseptic; traditional herbal poultices or collagen/alginate sheets may be layered over it
Barrier preparations Lassar’s paste largely replaced by zinc?oxide barrier creams or Unna boot; can be ordered from compounding pharmacies if desired",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2096,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar’s paste application around the ulcer,100.00%,1,Gemini 2.5,Gemini-2.5-flash-preview-05-20,5/1/2025,"CognetMD suggests A-C-E as correct choices. In early 2025, all LLMs picked A as the only answer. Gemini's (5/20/2025) comments about C and E: C: While it kills bacteria, it can be cytotoxic to healthy granulation tissue and may hinder the healing process of chronic wounds. Modern wound care generally avoids routine use of strong antiseptics on clean ulcers. E: Lassar's paste (zinc oxide paste) is a protective and mildly astringent paste often used for irritated skin or mild eczema. It could be helpful for the eczema around the ulcer by providing a barrier and soothing effect, but it does not treat the ulcer itself or the underlying cause of venous insufficiency. Would be rated as 1/3=33% according to the key, but should be 100% according to U guidelines",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2097,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar’s paste application around the ulcer,90.00%,1,ChatGPT 4.1,Chatgpt-4.1-mini-2025-04-14,5/1/2025,"CognetMD suggests A-C-E as correct choices. All LLMs picked A as the only answer. ChatGPT 4.1's comments about C and E: C: Not the first choice for venous ulcers.  E:  Lassar’s paste (zinc oxide and starch) is used for mild eczema and irritant dermatitis but is not a primary treatment for venous ulcers or venous eczema. (GPT suggest more up-to-date recommendations in US) Due to regional variations related to drug branding/availability, A is correct answer in USA",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2098,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer,90.00%,1,Mistral,mistral-small-2506,5/1/2025,"CognetMD suggests A-C-E as correct choices. The model suggested Best Answer: A, B, D, E (A and B are the most directly beneficial, while D and E may be supportive in specific cases.) Due to regional variations related to drug branding/availability, A-E is correct answer in USA",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Mistral series,88
2099,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer,100.00%,1,ChatGPT 4.1,ChatGPT-4.1,5/1/2025,"CognetMD suggests A-C-E as correct choices. ChatGPT 4.1 said: you must pick the MOST beneficial options, choose A and E, and B for the eczema, but not on the ulcer itself. If you want the best single answer, A is the most universally accepted. If you want all that apply: A, B (for eczema only), and E. C is occasionally useful: Use if infection present, not routinely (Antiseptics like povidone-iodine can be used on ulcers if there is evidence of infection, but routine use may delay healing.)  Due to regional variations related to drug branding/availability, A-E is correct answer in USA",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2100,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer,100.00%,1,Unkn,folsom-07172025-1,7/1/2025,"CognetMD suggests A-C-E as correct choices. The model's Answer: A & E Due to regional variations related to drug branding/availability, A-E is correct answer in USA",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2101,English,"CognetMD benchmark questions, Muiltiple-choice",Which disease can insects be vectors for? A. Cholera B. AIDS C. Bubonic Plague D. Leishmaniasis E. Lyme Disease Answer key: C-D-E,100.00%,1,Llama,llama-3.3-70b-instruct,5/1/2025,"Note that ofifcially given correct answer is C,D and E even though ticks are arachnids, not insects. Model's answer: Insects can be vectors for several diseases. The correct options from the given list are:* C. Bubonic Plague (fleas are the primary vectors) * D. Leishmaniasis (sandflies are the vectors) * E. Lyme Disease (ticks are the vectors) Insects or arthropods are not typically vectors for: * A. Cholera (usually spread through contaminated water or food) * B. AIDS (spread through bodily fluids, primarily through sexual contact or sharing needles). So, the diseases that insects can be vectors for from the given options are Bubonic Plague, Leishmaniasis, and Lyme Disease. (note that newer models - eg ChatGPT 4o, o1, Gemini 2 are excluding E since ticks are not insects)",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,LLaMA Series by Meta,88
2102,English,"CognetMD benchmark questions, Muiltiple-choice",Which disease can insects be vectors for? A. Cholera B. AIDS C. Bubonic Plague D. Leishmaniasis E. Lyme Disease Answer key: C-D-E,70.00%,1,Llama,llama-4-maverick-17b-128e-instruct,5/1/2025,"Note that ofifcially given correct answer is C,D and E even though ticks are arachnids, not insects. Model's answer: Given that the question is looking for a disease that insects can be vectors for and since both Bubonic Plague C and Leishmaniasis D are valid examples, we need to consider if the question implies a single answer or if it allows for multiple interpretations. Since ""D. Leishmaniasis"" is a correct example and directly answers the question without ambiguity regarding the vector being an insect, it is a straightforward choice. The best answer is D.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,LLaMA Series by Meta,88
2103,English,"CognetMD benchmark questions, Muiltiple-choice",Which disease can insects be vectors for? A. Cholera B. AIDS C. Bubonic Plague D. Leishmaniasis E. Lyme Disease Answer key: C-D-E,100.00%,1,ChatGPT o3,ChatGPT o3,5/1/2025,"Note that ofifcially given correct answer is C,D and E even though ticks are arachnids, not insects. Model's answer: Insects act as biological vectors for:* **Bubonic plague** – transmitted by the bites of infected rat fleas (*Xenopsylla cheopis* and relatives). * **Leishmaniasis** – transmitted by infected female sand flies (*Phlebotomus* or *Lutzomyia* spp.). The other choices are not spread by insects: * **Cholera** – mainly water/food borne. * **AIDS (HIV)** – spread through blood, sexual contact, etc., not by insects. * **Lyme disease** – transmitted by *Ixodes* ticks, which are arachnids, not insects. So the correct answers are **C. Bubonic Plague** and **D. Leishmaniasis**.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2104,English,"CognetMD benchmark questions, Muiltiple-choice",Which disease can insects be vectors for? A. Cholera B. AIDS C. Bubonic Plague D. Leishmaniasis E. Lyme Disease Answer key: C-D-E,100.00%,1,Command,command-a-03-2025,7/1/2025,"The correct answers are C, D, and E.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2105,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"B (A-B was defined as correct answer in the resource, but A is not the right answer. UVB phototherapy is not used as a treatment for acne in any country today.) Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2106,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,ChatGPT 4.1,ChatGPT 4.1; July 24 2025,5/1/2025,"Best answer: B. psoriasis  Vitiligo is also acceptable in some contexts, but psoriasis is the most universally correct for this list. ou may occasionally find very old reports (from the 1930s-1950s) from Europe or North America describing “sunlamp” or “ultraviolet” treatments for acne, but these did not distinguish UVA from UVB and are considered obsolete and potentially dangerous. No reputable clinic or dermatologist currently uses or recommends UVB for acne. Key-based score would be 50%. Human-Verified Score is 100%. A is also correct answer according to the key, but UVB phototherapy is not used as a treatment for acne in any country today.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2107,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,Gemini 2.5,gemini-2.5-flash: 7/24/2025,5/1/2025,"Both Psoriasis (B), Atopic Eczema (C), and Vitiligo (E) are conditions for which UVB is an appropriate treatment. However, in a multiple-choice question format where usually only one answer is expected, Psoriasis and Vitiligo are often considered the quintessential indications for UVB therapy. If forced to pick one from common clinical contexts, Psoriasis is a very strong and widely recognized indication. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2108,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,Qwen,qwen3-235b-a22b-instruct-2507: 7/24/2025,7/1/2025,"If the question allows multiple answers, B, C, and E are all appropriate. If only one is allowed, B. psoriasis is the most definitive answer. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2109,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,ChatGPT 4.1,ChatGPT 4.1; June 1 2025,5/1/2025,"The correct answers are: B. Psoriasis C. Atopic eczema (also known as atopic dermatitis) E. Vitiligo.  Psoriasis is effectively managed with UVB phototherapy, particularly narrowband UVB, due to its ability to reduce inflammation and slow down skin cell turnover.  Atopic eczema (atopic dermatitis) also responds well to UVB treatment because it reduces skin inflammation and itchiness. Vitiligo responds to narrowband UVB, which stimulates melanocyte repigmentation in affected skin areas.  Conditions like acne vulgaris and pityriasis versicolor typically do not benefit significantly from UVB phototherapy and have alternative preferred treatments. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2110,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,Gemini 2.0,gemini-2.0-flash-001,5/1/2025,B (B and E if asked for second best answer) Key-based score would be 50%. Human-Verified Score is 100%,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2111,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,Command,command-a-03-2025,7/1/2025,"B, C, and E. While some types of light therapy are used for acne, UVB is not typically the first-line treatment. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2112,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"B, C, and E. While some light - based therapies (e.g., blue light) may be used in specific cases, UVB is not a primary or well - established option for acne. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2113,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,ChatGPT 4.5,ChatGPT 4.5,5/1/2025,"B, C, and E. While some light - based therapies (e.g., blue light) may be used in specific cases, UVB is not a primary or well - established option for acne. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2114,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,ChatGPT o4,ChatGPT o4-mini,5/1/2025,"UVB phototherapy is indicated in a number of inflammatory and pigmentary dermatoses. Of the options listed, it’s appropriate for: B. Psoriasis – NB-UVB is a first-line phototherapy for chronic plaque psoriasis. C. Atopic eczema – Narrowband UVB can be used for moderate-to-severe atopic dermatitis. E. Vitiligo – NB-UVB is the treatment of choice for widespread vitiligo. It is not used for acne vulgaris (A), which relies on topical/systemic retinoids, antibiotics, etc.; nor for pityriasis versicolor (D), which is managed with antifungals. Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2115,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following conditions is UVB an appropriate treatment for? A. acne vulgaris B. psoriasis C. atopic eczema D. pityriasis versicolor E. vitiligo  Key: A-B,100.00%,1,Gemini 2.5,gemini-2.5-flash,5/1/2025,"The most appropriate responses where UVB is a well-established and common treatment are B. Psoriasis, C. Atopic Eczema, and E. Vitiligo Key-based score would be 50%. Human-Verified Score is 100%",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2116,English,"CognetMD benchmark questions, Muiltiple-choice",What are some prominent causes of erythema multiforme? A. Drugs B. Herpes zoster C. Streptococcal infections D. Primary tuberculosis E. Orf,100.00%,1,ChatGPT o4,ChatGPT o4-mini,5/1/2025,A-E,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2117,English,"CognetMD benchmark questions, Muiltiple-choice",Handling what substance could possibly lead to contact dermatitis? A. Chromate B. Cobalt C. Mercury D. Silver E. Aluminium: Key: A-B-C,100.00%,1,Command,command-a-03-2025,7/1/2025,"A,B,C: Handling the following substances can potentially lead to **contact dermatitis**: **A. Chromate**   **B. Cobalt**   **C. Mercury**  - **Chromate (A):** A well-known allergen and irritant, commonly found in cement, leather, and industrial materials.  - **Cobalt (B):** Often found in metal alloys, jewelry, and medical implants, and is a common cause of allergic contact dermatitis.  - **Mercury (C):** Used in some industrial processes, cosmetics (e.g., skin-lightening creams), and dental amalgam, and can cause both allergic and irritant contact dermatitis.  **D. Silver (D)** and **E. Aluminium (E)** are less commonly associated with contact dermatitis:  - Silver is generally inert and rarely causes dermatitis, though rare cases exist.  - Aluminium is more likely to cause irritant dermatitis in high concentrations but is not a common allergen.  Thus, the correct answers are **A, B,** and **C**.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2118,English,"CognetMD benchmark questions, Muiltiple-choice",Handling what substance could possibly lead to contact dermatitis? A. Chromate B. Cobalt C. Mercury D. Silver E. Aluminium: Key: A-B-C,100.00%,1,ChatGPT o3,ChatGPT o3-2025-04-16,5/1/2025,"A,B and C",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2119,English,"CognetMD benchmark questions, Muiltiple-choice",Handling what substance could possibly lead to contact dermatitis? A. Chromate B. Cobalt C. Mercury D. Silver E. Aluminium: Key: A-B-C,100.00%,1,Gemini 2.0,gemini-2.0-flash-001,5/1/2025,"A,B and C",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2120,English,"CognetMD benchmark questions, Muiltiple-choice",Handling what substance could possibly lead to contact dermatitis? A. Chromate B. Cobalt C. Mercury D. Silver E. Aluminium: Key: A-B-C,75.00%,1,Unkn,stephen,7/1/2025,B and C,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2121,English,"CognetMD benchmark questions, Muiltiple-choice",Which feature is least common in basal cell carcinomata? A. Pigmentation B. Scarring C. Cystic degeneration D. Hyperkeratosis E. Telangiectasia (Key: A-B-C),75.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,C (Cystic degeneration) < D (Hyperkeratosis) < B (Scarring),171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2122,English,"CognetMD benchmark questions, Muiltiple-choice",Which feature is least common in basal cell carcinomata? A. Pigmentation B. Scarring C. Cystic degeneration D. Hyperkeratosis E. Telangiectasia (Key: A-B-C),75.00%,1,Gemini 2.5,goldmane (Gemini 2.5 June 2025),5/1/2025,"D Atypical for BCC; a classic feature of SCC and actinic keratosis.; C Defines the uncommon nodulocystic BCC variant.' B Defines morpheaform BCC (~5-10% of cases); also seen with ulceration; A Defines pigmented BCC (~6-10% of cases); mimics melanoma; E. Telangiectasia A hallmark sign of nodular BCC, the most common subtype.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2123,English,"CognetMD benchmark questions, Muiltiple-choice",Which feature is least common in basal cell carcinomata? A. Pigmentation B. Scarring C. Cystic degeneration D. Hyperkeratosis E. Telangiectasia (Key: A-B-C),90.00%,1,Claude 4,Claude 4 Sonnet: claude-sonnet-4-20250514,5/1/2025,"Hyperkeratosis (D) - Least common < Cystic degeneration C < Scarring (B). Even though the key was A-B-C, D is  defensible: hyperkeratosis is not a hallmark of BCC and may lead to misdiagnosis (e.g., mistaking BCC for SCC or keratoses). But it’s still seen in superficial BCCs, and more often than cystic change.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Claude series,88
2124,English,"CognetMD benchmark questions, Muiltiple-choice",Which feature is least common in basal cell carcinomata? A. Pigmentation B. Scarring C. Cystic degeneration D. Hyperkeratosis E. Telangiectasia (Key: A-B-C),100.00%,1,Command,command-a-03-2025,7/1/2025,"C. Cystic degeneration This is the best, most accurate answer based on current understanding of BCC even though the key was A-B-C",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2125,English,"CognetMD benchmark questions, Muiltiple-choice",Which feature is least common in basal cell carcinomata? A. Pigmentation B. Scarring C. Cystic degeneration D. Hyperkeratosis E. Telangiectasia (Key: A-B-C),75.00%,1,Unkn,folsom-07172025-1,7/1/2025,"Answer: D. Hyperkeratosis. Answer key was A-B-C. However, D is  defensible: hyperkeratosis is not a hallmark of BCC and may lead to misdiagnosis (e.g., mistaking BCC for SCC or keratoses). But it’s still seen in superficial BCCs, and more often than cystic change.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2126,English,"CognetMD benchmark questions, Muiltiple-choice",Which feature is least common in basal cell carcinomata? A. Pigmentation B. Scarring C. Cystic degeneration D. Hyperkeratosis E. Telangiectasia (Key: A-B-C),75.00%,1,Gemini 2.5,Gemini 2.5 flash,5/1/2025,"Answer: D. Hyperkeratosis. Answer key was A-B-C. However, D is  defensible: hyperkeratosis is not a hallmark of BCC and may lead to misdiagnosis (e.g., mistaking BCC for SCC or keratoses). But it’s still seen in superficial BCCs, and more often than cystic change.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2127,English,"CognetMD benchmark questions, Muiltiple-choice",The Kobner phenomenon is associated with which of the following skin conditions? A. herpes zoster B. pityriasis rosea C. viral warts D. parapsoriasis E. molluscum contagiosum. Key: C,75.00%,1,Gemini 2.5,goldmane,5/1/2025,"Both C. Viral Warts and E. Molluscum Contagiosum are textbook examples of conditions that exhibit the Koebner phenomenon. The mechanism for both is autoinoculation of the causative virus into the skin via trauma (like scratching). In a multiple-choice question format, this represents a flawed question with two correct answers.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2128,English,"CognetMD benchmark questions, Muiltiple-choice",The Kobner phenomenon is associated with which of the following skin conditions? A. herpes zoster B. pityriasis rosea C. viral warts D. parapsoriasis E. molluscum contagiosum. Key: C,100.00%,1,Claude 4,Claude 4 Sonnet: claude-sonnet-4-20250514,5/1/2025,"C. viral warts Viral warts commonly exhibit the Kobner phenomenon. Trauma to the skin can lead to inoculation of human papillomavirus (HPV) into the injured area, resulting in new wart formation at the site of trauma. This is why warts often appear along scratch marks or areas of repeated irritation.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Claude series,88
2129,English,"CognetMD benchmark questions, Muiltiple-choice",The Kobner phenomenon is associated with which of the following skin conditions? A. herpes zoster B. pityriasis rosea C. viral warts D. parapsoriasis E. molluscum contagiosum. Key: C,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,C. viral warts. Fulfills the strict definition of the Koebner phenomenon. Classic textbook example.,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2130,English,"CognetMD benchmark questions, Muiltiple-choice",The Kobner phenomenon is associated with which of the following skin conditions? A. herpes zoster B. pityriasis rosea C. viral warts D. parapsoriasis E. molluscum contagiosum. Key: C,30.00%,1,Command,command-a-03-2025,7/1/2025,B. Pityriasis rosea. Incorrect; herald patch and non-trauma pathogenesis. No evidence of trauma-induced lesion development. Picking these indicates lack of familiarity with Koebner phenomenon.,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2131,English,"CognetMD benchmark questions, Muiltiple-choice",The Kobner phenomenon is associated with which of the following skin conditions? A. herpes zoster B. pityriasis rosea C. viral warts D. parapsoriasis E. molluscum contagiosum. Key: C,60.00%,1,Unkn,folsom-07172025-1,7/1/2025,"D. Parapsoriasis Weakly defensible; rare associations, likely a misinterpretation",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2132,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following are considered as pre-malignant skin lesions? A. Histiocytoma B. Seborrhoeic keratoses C. Parapsoriasis D. Solar keratoses E. Epidermal naevi Key: D-E,75.00%,1,Gemini 2.5,goldmane,5/1/2025,"D: In a typical exam setting asking for a single answer, D. Solar Keratoses would be the expected choice as it is the most prevalent and quintessential pre-malignant skin lesion. C. Parapsoriasis (Large Plaque) Pre-malignant (pre-lymphomatous) Mycosis Fungoides (CTCL). E. Epidermal Naevus Benign (with very low risk of secondary malignancy)",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2133,English,"CognetMD benchmark questions, Muiltiple-choice",Which of the following are considered as pre-malignant skin lesions? A. Histiocytoma B. Seborrhoeic keratoses C. Parapsoriasis D. Solar keratoses E. Epidermal naevi Key: D-E,75.00%,1,Claude 4,Claude 4 Sonnet: claude-sonnet-4-20250514,5/1/2025,"D. Solar keratoses (also called actinic keratoses) are considered premalignant lesions with the potential to progress to squamous cell carcinoma. They develop from chronic sun exposure and represent dysplastic changes in keratinocytes that can evolve into invasive carcinoma over time. // A. Histiocytoma (dermatofibroma) - Benign fibrous lesion, not premalignant B. Seborrhoeic keratoses - Benign epidermal tumors with virtually no malignant potential (though very rarely basal cell carcinoma can arise within them) C. Parapsoriasis - This is more complex. Large plaque parapsoriasis can progress to mycosis fungoides (cutaneous T-cell lymphoma), so it does have some premalignant potential, but it's less commonly recognized as a classic premalignant lesion compared to actinic keratoses E. Epidermal naevi - Generally benign developmental lesions, though very rarely malignant transformation can occur. Solar keratoses are the most clearly established premalignant skin lesions among these options, with a well-documented risk of progression to squamous cell carcinoma if left untreated",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Claude series,88
2134,English,"CognetMD benchmark questions, Muiltiple-choice",What are the five systemic diseases that can cause generalized pruritus? They include: A. carcinoid syndrome B. primary biliary cirrhosis C. chronic adrenal failure. D. systemic lymphoma E. obstructive jaundice. Key: B-D-E,75.00%,1,Gemini 2.5,Gemini 2.5 Flash,5/1/2025,"All of the listed conditions (A. Hodgkin's disease, B. Pityriasis rosea, C. Diabetes mellitus, D. Haemolytic jaundice, E. Polycythaemia rubra vera) can include generalized pruritus as a symptom.) Answeer given: B,D,E",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2135,English,"CognetMD benchmark questions, Muiltiple-choice",What are the five systemic diseases that can cause generalized pruritus? They include: A. carcinoid syndrome B. primary biliary cirrhosis C. chronic adrenal failure. D. systemic lymphoma E. obstructive jaundice. Key: B-D-E,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"B-D-E; A Carcinoid syndrome is less common.  Consider replacing C chronic adrenal failure with another known pruritus-associated condition such as: Chronic kidney disease (uremia), Polycythemia vera",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2136,English,"CognetMD benchmark questions, Muiltiple-choice",What are the five systemic diseases that can cause generalized pruritus? They include: A. carcinoid syndrome B. primary biliary cirrhosis C. chronic adrenal failure. D. systemic lymphoma E. obstructive jaundice. Key: B-D-E,83.00%,1,Command,command-a-03-2025,7/1/2025,B-D-E; Answered: A-B-C-D-E. I rated it as +1 for correct; –0.25 for incorrect 0 for blank: 2.5/3,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Cohere series,88
2137,English,"CognetMD benchmark questions, Muiltiple-choice",What are the five systemic diseases that can cause generalized pruritus? They include: A. carcinoid syndrome B. primary biliary cirrhosis C. chronic adrenal failure. D. systemic lymphoma E. obstructive jaundice. Key: B-D-E,100.00%,1,Unkn,folsom-07172025-1,7/1/2025,B-D-E; ,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2138,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer  KEy: A-C-E,75.00%,1,Unkn,X-preview,7/1/2025,"A,C,B,E: A. The usage of a supportive elastic bandage. Compression therapy is the cornerstone of managing venous ulcers, improving venous return and reducing edema. While E. Lassar's paste application around the ulcer protects periwound skin, and B. Betnovate-C ointment may address eczema, compression (A) is primary for ulcer healing. Avoiding potent steroids (B) directly on the ulcer and long-term antiseptics (C) aligns with best practices. Diuretics (D) are less relevant unless systemic edema is present.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2139,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer  KEy: A-C-E,75.00%,1,Gemini 2.0,gemini-2.0-flash-001,5/1/2025,"A,E: The most beneficial option, addressing the underlying cause of the ulcer, is A. The usage of a supportive elastic bandage. E is also useful as an adjunct.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2140,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer  KEy: A-C-E,50.00%,1,Gemini 2.5,gemini-2.5-flash-preview-05-20,5/1/2025,A vs A-C-E as indicated by th author of benchmarks used,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Google's Family of LLMs,88
2141,English,"CognetMD benchmark questions, Muiltiple-choice",Which among the following options might prove beneficial for an old man suffering from a gravitational ulcer with adjacent eczema? A. The usage of a supportive elastic bandage B. Daily application of Betnovate-C ointment C. Povidine-iodine ointment on the ulcer D. Diuretic therapy for oedema reduction E. Lassar's paste application around the ulcer,50.00%,1,ChatGPT 4.1,Chatgpt-4.1-mini-2025-04-14,5/1/2025,A vs A-C-E as indicated by th author of benchmarks used,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2142,English,"CognetMD benchmark questions, Muiltiple-choice","Which of the following statements accurately describe Tuberose sclerosis? A. It induces telangiectatic lesions on the face. B. It results in periungual fibromas C. It leads to patchy hypopigmentation noticeable from birth D. It is more common in males E. It is invariably linked with epilepsy or mental deficiency, or both",100.00%,1,Grok,grok-4-0709,5/1/2025,"A-B-C. Answered: B and C.  B. It results in periungual fibromas: Accurate. Periungual fibromas (also called Koenen tumors) are a hallmark cutaneous feature of TSC. These are small, fleshy growths that develop around or under the fingernails or toenails, typically appearing in adolescence or adulthood. C. It leads to patchy hypopigmentation noticeable from birth: Accurate. Hypopigmented macules (ash-leaf spots) are one of the earliest and most common skin findings in TSC. These lancet-shaped or confetti-like patches of hypopigmentation are often present at birth or appear in early infancy and can be detected under Wood's lamp examination.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,xAI Grok series,88
2143,English,"CognetMD benchmark questions, Muiltiple-choice","Which of the following statements accurately describe Tuberose sclerosis? A. It induces telangiectatic lesions on the face. B. It results in periungual fibromas C. It leads to patchy hypopigmentation noticeable from birth D. It is more common in males E. It is invariably linked with epilepsy or mental deficiency, or both",100.00%,1,Unkn,clownfish,7/1/2025,"A-B-C  Answered: A, B ans C: A. It induces telangiectatic lesions on the face – (Facial angiofibromas are a hallmark feature.); B. It results in periungual fibromas – (Koenen tumors are common in TSC.); C. It leads to patchy hypopigmentation noticeable from birth – (Ash-leaf spots are often present at birth.)",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2144,English,"CognetMD benchmark questions, Muiltiple-choice","Which of the following statements accurately describe Tuberose sclerosis? A. It induces telangiectatic lesions on the face. B. It results in periungual fibromas C. It leads to patchy hypopigmentation noticeable from birth D. It is more common in males E. It is invariably linked with epilepsy or mental deficiency, or both",100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A-B-C was key; Answered B and C. B. It results in periungual fibromas Classic, specific, and unambiguous C. It leads to patchy hypopigmentation noticeable from birth Correct and important, but less specific than B (since other conditions also present with hypopigmentation). Stated that answering C, Misidentifies lesion type means partial confusion",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2145,English,"CognetMD benchmark questions, Muiltiple-choice","Which of the following statements accurately describe Tuberose sclerosis? A. It induces telangiectatic lesions on the face. B. It results in periungual fibromas C. It leads to patchy hypopigmentation noticeable from birth D. It is more common in males E. It is invariably linked with epilepsy or mental deficiency, or both",100.00%,1,ChatGPT 4.5,ChatGPT-4.5,5/1/2025,"A-B-C was key; Answered B and C. A (Incorrect): Facial telangiectasias are more characteristic of conditions like rosacea or hereditary hemorrhagic telangiectasia. In tuberous sclerosis, facial lesions typically include adenoma sebaceum (angiofibromas) rather than telangiectatic lesions. B (Correct): Periungual fibromas, also known as Koenen tumors, are a common dermatologic manifestation of tuberous sclerosis. C (Correct): Hypopigmented macules, often called ""ash-leaf spots,"" are a hallmark of tuberous sclerosis, typically present from birth or appearing shortly thereafter.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2146,English,"CognetMD benchmark questions, Muiltiple-choice","Which of the following statements accurately describe Tuberose sclerosis? A. It induces telangiectatic lesions on the face. B. It results in periungual fibromas C. It leads to patchy hypopigmentation noticeable from birth D. It is more common in males E. It is invariably linked with epilepsy or mental deficiency, or both",100.00%,1,Copilot,Copilot,7/1/2025,"A-B-C was key; Answered B and C.  TSC causes facial angiofibromas, which may appear reddish and vascular but are not purely telangiectatic. They are fibrous and vascular growths, not simple telangiectasias",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2147,English,"CognetMD benchmark questions, Muiltiple-choice","Adverse reations to drugs A. Are termed ""fixed"" when they occur every time the drug is administered B. Most commonly manifest as an eczematous eruption C. Show a tendency to become more severe every time the drug is used - Are drug adverse reactions D. Might also look like lichen planus E. Are always due to an allergic reaction",80.00%,1,Grok,grok-4-0709,5/1/2025,"D (Answer was A, C and D)",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,xAI Grok series,88
2148,English,"CognetMD benchmark questions, Muiltiple-choice","Adverse reations to drugs A. Are termed ""fixed"" when they occur every time the drug is administered B. Most commonly manifest as an eczematous eruption C. Show a tendency to become more severe every time the drug is used - Are drug adverse reactions D. Might also look like lichen planus E. Are always due to an allergic reaction",90.00%,1,Unkn,clownfish,7/1/2025,D (Answer was A and D),171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,unknown (LLM arena),88
2149,English,"CognetMD benchmark questions, Muiltiple-choice","Adverse reations to drugs A. Are termed ""fixed"" when they occur every time the drug is administered B. Most commonly manifest as an eczematous eruption C. Show a tendency to become more severe every time the drug is used - Are drug adverse reactions D. Might also look like lichen planus E. Are always due to an allergic reaction",100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"D (Answer was D. Also noted that C is partially true in Type I hypersensitivity reactions (e.g., anaphylaxis), but not always — not all drug reactions escalate; A could be acceptable, but phrasing is imprecise)",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2150,English,"CognetMD benchmark questions, Muiltiple-choice","The atopic state often results in atopic eczema, though it is roughly only half as frequent as asthma, Can it present itself without irritation? Is the range of allergens responsible for it identical to those causing asthma? Do non-sedating antihistamines effectively treat it? And, is it common for it to affect the faces of young infants? A. Young infants' faces are often affected by it. B. Non-sedating antihistamines respond well to its treatment. C. It is approximately half as common as asthma, D. Even in the absence of irritation it may be present, E. The same allergens that cause asthma are responsible for atopic eczema.",100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A Young infants' faces are often affected by atopic eczema (also known as atopic dermatitis). This is a well-established, clinically distinctive feature of atopic eczema and often helps in its early diagnosis. It’s specific, observable, and strongly associated with the condition — especially compared to more general or overlapping statements like shared allergens or relative prevalence.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,88
2151,English,"CognetMD benchmark questions, Muiltiple-choice","The atopic state often results in atopic eczema, though it is roughly only half as frequent as asthma, Can it present itself without irritation? Is the range of allergens responsible for it identical to those causing asthma? Do non-sedating antihistamines effectively treat it? And, is it common for it to affect the faces of young infants? A. Young infants' faces are often affected by it. B. Non-sedating antihistamines respond well to its treatment. C. It is approximately half as common as asthma, D. Even in the absence of irritation it may be present, E. The same allergens that cause asthma are responsible for atopic eczema.",100.00%,1,Grok,grok-4-0709,5/1/2025,"A. In infants, atopic eczema classically affects the face, particularly the cheeks, forehead, and chin. This is one of the hallmark presentations in babies aged 3-6 months. The facial distribution is so characteristic that it's part of the typical pattern recognition for infantile eczema. Only A is unequivocally true based on medical evidence. The others are false or inaccurate.",171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,xAI Grok series,88
2152,English,"CognetMD benchmark questions, Muiltiple-choice","The atopic state often results in atopic eczema, though it is roughly only half as frequent as asthma, Can it present itself without irritation? Is the range of allergens responsible for it identical to those causing asthma? Do non-sedating antihistamines effectively treat it? And, is it common for it to affect the faces of young infants? A. Young infants' faces are often affected by it. B. Non-sedating antihistamines respond well to its treatment. C. It is approximately half as common as asthma, D. Even in the absence of irritation it may be present, E. The same allergens that cause asthma are responsible for atopic eczema.",100.00%,1,Claude 4,claude-opus-4-20250514,5/1/2025,Only A is correct.,171 dermatology questions out of 542 Medical domain-specific questions (MCQs) ,"Panagoulias, D.P. et al. (2025). COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. In: Nagar, A., Jat, D.S., Mishra, D., Joshi, A. (eds) Intelligent Sustainable Systems. Worlds4 2024. Lecture Notes in Networks and Systems, vol 1178. Springer, Singapore. https://doi.org/10.1007/978-981-97-9559-8_38  https://huggingface.co/datasets/DimitriosPanagoulias/COGNET-MD/viewer/default/train?q=dermatology&views%5B%5D=train",COGnitive Network Evaluation Toolkit for Medical Domains: Evaluating Large Language Model Performance in Medical Diagnostics—An Assessment Framework and Dataset for Healthcare AI. ,Dermatology Examinations and Practice Questions,Professional Education,2025,Claude series,88
2153,English,"Scientific accuracy of Clinical Vignette (used in teaching to test clinical reasoning, diagnostic skills, and decision-making) generated by LLM / also assessed for alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias, and overall quality.","Scarlet Fever, Local Subcutaneous Reaction, Hyperhidrosis, Stomatitis, Acne Vulgaris, Staphylococcal Scalded Skin Syndrome, Ichthyosis, Impetigo, Stevens-Johnson Syndrome, Tinea Corporis, Cauliflower Ear, Dermatoses Caused by Plants, Lentigo, Frostbite, Melanoma, Herpes Zoster, Cellulitis, Folliculitis, Pigmented Nevi, and Keloids",89.00%,60,ChatGPT 4,ChatGPT-4,3/14/2023,"Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r?=?0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. ","60 ratings by three attending physicians of the twenty clinical vignettes based on alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias.","Rao AS, Kim J, Mu A, Young CC, Kalmowitz E, Senter-Zapata M, Whitehead DC, Garibyan L, Landman AB, Succi MD. Synthetic medical education in dermatology leveraging generative artificial intelligence. NPJ Digit Med. 2025 May 4;8(1):247. doi: 10.1038/s41746-025-01650-x. PMID: 40320492; PMCID: PMC12050279.",Synthetic medical education in dermatology leveraging generative artificial intelligence,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,72
2154,English,"Comprehensiveness of Clinical Vignette (used in teaching to test clinical reasoning, diagnostic skills, and decision-making) generated by LLM / also assessed for alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias, and overall quality.","Scarlet Fever, Local Subcutaneous Reaction, Hyperhidrosis, Stomatitis, Acne Vulgaris, Staphylococcal Scalded Skin Syndrome, Ichthyosis, Impetigo, Stevens-Johnson Syndrome, Tinea Corporis, Cauliflower Ear, Dermatoses Caused by Plants, Lentigo, Frostbite, Melanoma, Herpes Zoster, Cellulitis, Folliculitis, Pigmented Nevi, and Keloids",80.00%,60,ChatGPT 4,ChatGPT-4,3/14/2023,"Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), c (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r?=?0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. ","60 ratings by three attending physicians of the twenty clinical vignettes based on alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias.","Rao AS, Kim J, Mu A, Young CC, Kalmowitz E, Senter-Zapata M, Whitehead DC, Garibyan L, Landman AB, Succi MD. Synthetic medical education in dermatology leveraging generative artificial intelligence. NPJ Digit Med. 2025 May 4;8(1):247. doi: 10.1038/s41746-025-01650-x. PMID: 40320492; PMCID: PMC12050279.",Synthetic medical education in dermatology leveraging generative artificial intelligence,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,72
2155,English,"Overall quality of Clinical Vignette (used in teaching to test clinical reasoning, diagnostic skills, and decision-making) generated by LLM / also assessed for alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias.","Scarlet Fever, Local Subcutaneous Reaction, Hyperhidrosis, Stomatitis, Acne Vulgaris, Staphylococcal Scalded Skin Syndrome, Ichthyosis, Impetigo, Stevens-Johnson Syndrome, Tinea Corporis, Cauliflower Ear, Dermatoses Caused by Plants, Lentigo, Frostbite, Melanoma, Herpes Zoster, Cellulitis, Folliculitis, Pigmented Nevi, and Keloids",85.60%,60,ChatGPT 4,ChatGPT-4,3/14/2023,"Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r?=?0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. ","60 ratings by three attending physicians of the twenty clinical vignettes based on alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias.","Rao AS, Kim J, Mu A, Young CC, Kalmowitz E, Senter-Zapata M, Whitehead DC, Garibyan L, Landman AB, Succi MD. Synthetic medical education in dermatology leveraging generative artificial intelligence. NPJ Digit Med. 2025 May 4;8(1):247. doi: 10.1038/s41746-025-01650-x. PMID: 40320492; PMCID: PMC12050279.",Synthetic medical education in dermatology leveraging generative artificial intelligence,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,72
2156,English,"Potential clinical harm of Clinical Vignette (used in teaching to test clinical reasoning, diagnostic skills, and decision-making) generated by LLM / also assessed for alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias, and overall quality.","Scarlet Fever, Local Subcutaneous Reaction, Hyperhidrosis, Stomatitis, Acne Vulgaris, Staphylococcal Scalded Skin Syndrome, Ichthyosis, Impetigo, Stevens-Johnson Syndrome, Tinea Corporis, Cauliflower Ear, Dermatoses Caused by Plants, Lentigo, Frostbite, Melanoma, Herpes Zoster, Cellulitis, Folliculitis, Pigmented Nevi, and Keloids",32.00%,60,ChatGPT 4,ChatGPT-4,3/14/2023,"Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r?=?0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. ","60 ratings by three attending physicians of the twenty clinical vignettes based on alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias.","Rao AS, Kim J, Mu A, Young CC, Kalmowitz E, Senter-Zapata M, Whitehead DC, Garibyan L, Landman AB, Succi MD. Synthetic medical education in dermatology leveraging generative artificial intelligence. NPJ Digit Med. 2025 May 4;8(1):247. doi: 10.1038/s41746-025-01650-x. PMID: 40320492; PMCID: PMC12050279.",Synthetic medical education in dermatology leveraging generative artificial intelligence,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,72
2157,English,"Demographic bias of Clinical Vignette (used in teaching to test clinical reasoning, diagnostic skills, and decision-making) generated by LLM / also assessed for alignment with scientific consensus, possibility of clinical harm, comprehensiveness,  and overall quality.","Scarlet Fever, Local Subcutaneous Reaction, Hyperhidrosis, Stomatitis, Acne Vulgaris, Staphylococcal Scalded Skin Syndrome, Ichthyosis, Impetigo, Stevens-Johnson Syndrome, Tinea Corporis, Cauliflower Ear, Dermatoses Caused by Plants, Lentigo, Frostbite, Melanoma, Herpes Zoster, Cellulitis, Folliculitis, Pigmented Nevi, and Keloids",30.40%,60,ChatGPT 4,ChatGPT-4,3/14/2023,"Physician experts gave the vignettes high average scores on a Likert scale in scientific accuracy (4.45/5), comprehensiveness (4.3/5), and overall quality (4.28/5) and low scores for potential clinical harm (1.6/5) and demographic bias (1.52/5). A strong correlation (r?=?0.83) was observed between comprehensiveness and overall quality. Vignettes did not incorporate significant demographic diversity. ","60 ratings by three attending physicians of the twenty clinical vignettes based on alignment with scientific consensus, possibility of clinical harm, comprehensiveness, possibility of demographic bias.","Rao AS, Kim J, Mu A, Young CC, Kalmowitz E, Senter-Zapata M, Whitehead DC, Garibyan L, Landman AB, Succi MD. Synthetic medical education in dermatology leveraging generative artificial intelligence. NPJ Digit Med. 2025 May 4;8(1):247. doi: 10.1038/s41746-025-01650-x. PMID: 40320492; PMCID: PMC12050279.",Synthetic medical education in dermatology leveraging generative artificial intelligence,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,72
2158,Turkish,first year Dermatology Exam,General Dermatology,68.00%,25,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2159,Turkish,second year Dermatology Exam,General Dermatology,64.00%,25,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2160,Turkish,third year Dermatology Exam,General Dermatology,52.00%,25,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2161,Turkish,fourth year Dermatology Exam,General Dermatology,40.00%,25,ChatGPT 3.5,ChatGPT 3.5,11/30/2022,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2162,Turkish,first year Dermatology Exam,General Dermatology,72.00%,25,ChatGPT 4,ChatGPT-4,3/14/2023,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2163,Turkish,second year Dermatology Exam,General Dermatology,68.00%,25,ChatGPT 4,ChatGPT-4,3/14/2023,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2164,Turkish,third year Dermatology Exam,General Dermatology,64.00%,25,ChatGPT 4,ChatGPT-4,3/14/2023,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2165,Turkish,fourth year Dermatology Exam,General Dermatology,48.00%,25,ChatGPT 4,ChatGPT-4,3/14/2023,"ChatGPT 3.5 passed the first and second?year exams but failed the third and fourth?year exams. ChatGPT 4.0 passed the first, second, and third?year exams but failed the fourth?year exam. Pediatric dermatology clinicians generally outperformed both versions of ChatGPT; however, the performance of ChatGPT?4.0 was comparable in some areas.","100 questions were created in Turkish, divided into four groups of 25 questions each, based on the seniority of dermatology resident physicians","Göçer Gürok N, Öztürk S. The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT. J Cosmet Dermatol. 2025 May;24(5):e70244. doi: 10.1111/jocd.70244. PMID: 40387311; PMCID: PMC12087418.",The Performance of AI in Dermatology Exams: The Exam Success and Limits of ChatGPT.,Dermatology Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,73
2166,English,"Realistic, rubric-based evaluation of AI model performance in multi-turn health-related conversations.: Mild breakouts, gradually worse over 6 months - in an adult; Adult acne",Acne,76.19%,15,Gemini 2.5,Gemini-2.5-flash-preview-04-17,5/1/2025,"mild breakouts, gradually worse over 6 months.  - described prescription options and appropriately framed as things a doctor would prescribe but  was noticeably longer and more technical thus penalized for communication","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
2167,English,"Realistic, rubric-based evaluation of AI model performance in multi-turn health-related conversations.: Adult acne",Acne,87.30%,15,ChatGLM,Glm-4-air-250414,5/1/2025,"mild breakouts, gradually worse over 6 months.  - described prescription options and appropriately framed as things a doctor would prescribe.","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Zhipu AI family of models,88
2168,English,"Realistic, rubric-based evaluation of AI model performance in multi-turn health-related conversations.","Hair loss, hair shedding",88.06%,15,Claude 4,Claude 4 Opus: claude-opus-4-20250514,5/1/2025,"I just noticed a bunch of hair coming out on my brush this morning It’s normal to shed some hair throughout the day, but if you’re seeing more than usual, it can be unsettling. Sometimes a sudden increase in shedding can be related to stress or changes in diet, but other factors could be at play as well. Are there any other changes you’ve noticed? It just feels so sudden, and my scalp is itchy too. I’m scared it means something is seriously wrong. I can’t stop thinking about it. An itchy scalp can have many causes, from mild irritation to certain skin conditions, and stress itself can sometimes exacerbate hair shedding. It’s understandable to feel worried if you see more hair than usual. Have you been under more stress than usual, or changed your hair care routine? Could it be a serious illness? I’m so anxious I can’t even sleep, I feel like I’m going to lose all my hair if this continues.","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Claude series,88
2169,English,"Realistic, rubric-based evaluation of AI model performance in multi-turn health-related conversations.","Hair loss, hair shedding",83.58%,15,Unkn,Redsword,7/1/2025,"I just noticed a bunch of hair coming out on my brush this morning It’s normal to shed some hair throughout the day, but if you’re seeing more than usual, it can be unsettling. Sometimes a sudden increase in shedding can be related to stress or changes in diet, but other factors could be at play as well. Are there any other changes you’ve noticed? It just feels so sudden, and my scalp is itchy too. I’m scared it means something is seriously wrong. I can’t stop thinking about it. An itchy scalp can have many causes, from mild irritation to certain skin conditions, and stress itself can sometimes exacerbate hair shedding. It’s understandable to feel worried if you see more hair than usual. Have you been under more stress than usual, or changed your hair care routine? Could it be a serious illness? I’m so anxious I can’t even sleep, I feel like I’m going to lose all my hair if this continues.","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
2170,English,Patient-Centric Clinical Education,"Sunburn: my sister got a really bad sunburn yesterday, her skin is so red and she's in a lot of pain. do you think her sunburn will turn into a tan after it heals?",48.28%,15,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Ranked according to 15 criteria given. The response is overall accurate, understandable, and helpful, but it misses an important educational point about melanin and inappropriately recommended ibuprofen without disclaimers. Answer:  a bad sunburn may eventually lead to some tanning, but it's not guaranteed and depends on her skin type and how deep the burn is. Here's how it generally works: Mild sunburns (redness, mild discomfort) sometimes fade into a tan as the skin recovers. Severe sunburns (intense redness, pain, peeling, blistering) often cause skin damage that peels, and any tan that appears might be patchy or minimal because the top layers of skin are shedding. Most importantly, a bad sunburn increases the risk of skin damage and long-term issues, including premature aging and skin cancer. She should: Stay out of the sun while healing. Use soothing treatments like aloe vera or cool compresses. Stay hydrated. Take over-the-counter pain relief if needed (like ibuprofen) If she has blisters, a fever, chills, or feels unwell, she should see a doctor.","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
2171,English,Patient-Centric Clinical Education,"Sunburn: my sister got a really bad sunburn yesterday, her skin is so red and she's in a lot of pain. do you think her sunburn will turn into a tan after it heals?",31.03%,15,Claude 4,Claude 4 Sonnet,5/1/2025,"Ranked according to 15 criteria given. This response is safe, clear, and practical, but it misses key educational value by not discussing melanin or the long-term risks of sunburn. It also makes an unqualified medication recommendation, which is risky. Answer: Unfortunately, a severe sunburn like you're describing typically won't turn into a tan. When skin gets badly burned and turns very red, it usually means the damage goes deeper than the superficial layer where tanning occurs. Instead of tanning, badly sunburned skin typically peels as it heals, revealing new skin underneath that's often lighter than before. For her immediate comfort, she can try cool compresses, aloe vera gel, or over-the-counter pain relievers like ibuprofen to help with both pain and inflammation. Staying well-hydrated is important too. If the burn is severe, covers a large area, or if she develops fever, chills, or blistering, it would be worth having a doctor take a look. The good news is that sunburns do heal, though it might take a week or two depending on the severity. Once she's feeling better, she'll want to be extra careful about sun protection since sunburned skin is more sensitive for a while.","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Claude series,88
2172,English,Patient-Centric Clinical Education,"Sunburn: my sister got a really bad sunburn yesterday, her skin is so red and she's in a lot of pain. do you think her sunburn will turn into a tan after it heals?",65.52%,15,Gemini 2.5,Gemini 2.5 June 12 version A an A/B test setting,5/1/2025,"Ranked according to 15 criteria given. Overall, the response is medically accurate, comprehensive, and well-tailored for a lay audience, but loses points for recommending NSAIDs without any patient-specific contraindication assessment.","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
2173,English,Patient-Centric Clinical Education,"Sunburn: my sister got a really bad sunburn yesterday, her skin is so red and she's in a lot of pain. do you think her sunburn will turn into a tan after it heals?",31.03%,15,Gemini 2.5,Gemini 2.5 June 12 version B an A/B test setting,5/1/2025,Ranked according to 15 criteria given. ,"64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
2174,English,Patient-Centric Clinical Education,"Sunburn: my sister got a really bad sunburn yesterday, her skin is so red and she's in a lot of pain. do you think her sunburn will turn into a tan after it heals?",31.03%,15,ChatGPT o4,ChatGPT-o4-mini-2025-04-16,5/1/2025,"Ranked according to 15 criteria given. the response was warm, accessible, and empowering tone with Helpful practical suggestions (cool compresses, aloe, sunscreen) BUT No mention of serious symptoms that require medical attention; No reference to long-term skin damage or cancer risk; Recommends NSAIDs without context, which violates safety guidelines","64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
2175,English,Patient-Centric Clinical Education,"Sunburn: my sister got a really bad sunburn yesterday, her skin is so red and she's in a lot of pain. do you think her sunburn will turn into a tan after it heals?",100.00%,15,Gemini 2.5,goldmane,5/1/2025,Ranked according to 15 criteria given. ,"64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
2176,English,Patient-Centric Clinical Education,"Vascular eczema: stasis dermatitis: my dads legs are red n itchy and u can see the veins, is that vascular eczema?",96.77%,15,ChatGPT o4,ChatGPT-o4-mini-2025-04-16,5/1/2025,Ranked according to 15 criteria given. ,"64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,88
2177,English,Patient-Centric Clinical Education,"Vascular eczema: stasis dermatitis: my dads legs are red n itchy and u can see the veins, is that vascular eczema?",99.19%,15,Gemini 2.5,goldmane,5/1/2025,Ranked according to 15 criteria given. ,"64 dermatology questitons from HealthBench's 5,000 realistic health conversations, each with a custom physician-created rubric to grade model responses. (15 criteria for validity score used in this dataset)","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)  Benchmarks from: Arora RK, Wei J, Hicks RS, Bowman P, Quiñonero-Candela J, Tsimpourlas F, Sharman M, Shah M, Vallone A, Beutel A, Heidecke J. HealthBench: Evaluating Large Language Models Towards Improved Human Health. arXiv preprint arXiv:2505.08775. 2025 May 13. https://doi.org/10.48550/arXiv.2505.08775",HealthBench: Evaluating Large Language Models Towards Improved Human Health.,Medical Records and Diagnostic Processes,Clinical Practice,2025,Google's Family of LLMs,88
2178,English,Disclaimer Presence in Medical Q&A,Benign skin conditions: and insect bites,100.00%,5,ChatGPT 4o,ChatGPT-4o,5/13/2024,"""I can't diagnose skin conditions or provide a medical opinion from images… General Observations (Educational Only — Not a Diagnosis)..."", """"Educational Use"" ","5 images randomly selected from The International Skin Imaging Collaboration, dermatological journals and personal collection","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,,,2025,OpenAI GPT series,88
2179,English,Disclaimer Presence in Medical Q&A,Benign skin conditions: and insect bites,100.00%,5,ChatGPT o4,ChatGPT o4-mini,5/1/2025,"""Educational Observations (Not a Diagnosis)""","5 images randomly selected from The International Skin Imaging Collaboration, dermatological journals and personal collection","Gabashvili, Language Models in Dermatology: A Systematic Review of Quantitative Evaluations with Meta-analysis (this work)",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,,,2025,OpenAI GPT series,88
2180,"English, Romanian",Single Diagnosis (Image Only),MELANOMA,50.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2181,"English, Romanian",Top 2 Diagnoses (Image Only),MELANOMA,53.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2182,"English, Romanian",Top 3 Diagnoses (Image Only),MELANOMA,63.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2183,"English, Romanian",Top 4 Diagnoses (Image Only),MELANOMA,93.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2184,"English, Romanian",Single Diagnosis (Image+Patient Data),MELANOMA,60.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2185,"English, Romanian",Top 2 Diagnoses (Image+Patient Data),MELANOMA,83.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2186,"English, Romanian",Top 3 Diagnoses (Image+Patient Data),MELANOMA,100.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2187,"English, Romanian",Top 4 Diagnoses (Image+Patient Data),MELANOMA,100.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2188,"English, Romanian",Single Diagnosis (Image Only),NEVUS,40.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2189,"English, Romanian",Top 2 Diagnoses (Image Only),NEVUS,76.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2190,"English, Romanian",Top 3 Diagnoses (Image Only),NEVUS,93.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2191,"English, Romanian",Top 4 Diagnoses (Image Only),NEVUS,96.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2192,"English, Romanian",Single Diagnosis (Image+Patient Data),NEVUS,46.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2193,"English, Romanian",Top 2 Diagnoses (Image+Patient Data),NEVUS,96.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2194,"English, Romanian",Top 3 Diagnoses (Image+Patient Data),NEVUS,100.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2195,"English, Romanian",Top 4 Diagnoses (Image+Patient Data),NEVUS,100.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2196,"English, Romanian",Single Diagnosis (Image Only),SK (Seborrheic Keratosis),4.50%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2197,"English, Romanian",Top 2 Diagnoses (Image Only),SK (Seborrheic Keratosis),36.50%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2198,"English, Romanian",Top 3 Diagnoses (Image Only),SK (Seborrheic Keratosis),45.20%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2199,"English, Romanian",Top 4 Diagnoses (Image Only),SK (Seborrheic Keratosis),63.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2200,"English, Romanian",Single Diagnosis (Image+Patient Data),SK (Seborrheic Keratosis),20.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2201,"English, Romanian",Top 2 Diagnoses (Image+Patient Data),SK (Seborrheic Keratosis),24.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2202,"English, Romanian",Top 3 Diagnoses (Image+Patient Data),SK (Seborrheic Keratosis),24.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2203,"English, Romanian",Top 4 Diagnoses (Image+Patient Data),SK (Seborrheic Keratosis),43.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2204,"English, Romanian",Single Diagnosis (Image Only),BCC (Basal Cell Carcinoma),63.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2205,"English, Romanian",Top 2 Diagnoses (Image Only),BCC (Basal Cell Carcinoma),70.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2206,"English, Romanian",Top 3 Diagnoses (Image Only),BCC (Basal Cell Carcinoma),90.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2207,"English, Romanian",Top 4 Diagnoses (Image Only),BCC (Basal Cell Carcinoma),93.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2208,"English, Romanian",Single Diagnosis (Image+Patient Data),BCC (Basal Cell Carcinoma),30.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2209,"English, Romanian",Top 2 Diagnoses (Image+Patient Data),BCC (Basal Cell Carcinoma),66.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2210,"English, Romanian",Top 3 Diagnoses (Image+Patient Data),BCC (Basal Cell Carcinoma),90.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2211,"English, Romanian",Top 4 Diagnoses (Image+Patient Data),BCC (Basal Cell Carcinoma),100.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2212,"English, Romanian",Single Diagnosis (Image Only),TOTAL,39.70%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2213,"English, Romanian",Top 2 Diagnoses (Image Only),TOTAL,59.00%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2214,"English, Romanian",Top 3 Diagnoses (Image Only),TOTAL,73.50%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2215,"English, Romanian",Top 4 Diagnoses (Image Only),TOTAL,86.50%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2216,"English, Romanian",Single Diagnosis (Image+Patient Data),TOTAL,39.00%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2217,"English, Romanian",Top 2 Diagnoses (Image+Patient Data),TOTAL,67.70%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2218,"English, Romanian",Top 3 Diagnoses (Image+Patient Data),TOTAL,78.00%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2219,"English, Romanian",Top 4 Diagnoses (Image+Patient Data),TOTAL,86.00%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"A generated answer was considered  correct if it corresponded to the actual diagnosis (as all the 120 cases had diagnoses  confirmed through histopathology). Cases were input sequentially, and a response was generated for each. The metadata used in Test 3 and Test 4 included: age, sex, family history of melanoma, personal history of melanoma, lesion size in mm (if specified), and general anatomic site of the lesion (if specified).","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2220,"English, Romanian",Accuracy of Multiple Choice Tests (Image Only),MELANOMA,36.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2221,"English, Romanian",Multiple Choice (Image+Patient Data),MELANOMA,50.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2222,"English, Romanian",Accuracy of Multiple Choice Tests (Image Only),NEVUS,46.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2223,"English, Romanian",Multiple Choice (Image+Patient Data),NEVUS,40.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2224,"English, Romanian",Accuracy of Multiple Choice Tests (Image Only),SK (Seborrheic Keratosis),16.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2225,"English, Romanian",Multiple Choice (Image+Patient Data),SK (Seborrheic Keratosis),20.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2226,"English, Romanian",Accuracy of Multiple Choice Tests (Image Only),BCC (Basal Cell Carcinoma),42.70%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2227,"English, Romanian",Multiple Choice (Image+Patient Data),BCC (Basal Cell Carcinoma),42.70%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2228,"English, Romanian",Accuracy of Multiple Choice Tests (Image Only),TOTAL,36.00%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2229,"English, Romanian",Multiple Choice (Image+Patient Data),TOTAL,38.00%,120,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Results showed a better performance in the Free Answer format, as opposed to Multiple Choice. Furthermore, adding patient data to the input images did not improve accuracy (except in some cases). free-response multiple possibilities approach may be more effective in producing reliable outputs. This kind of  approach could be particularly beneficial for students that are eager to develop the diagnostic reasoning skills, as they can also ask the chatbot about the logic behind every choice. ","120 dermoscopic images (30 per class) from the International Skin Imaging Collaboration Archive, with histopathology-confirmed labels","Gudiu AV, Stoicu-Tivadar L. ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability. Stud Health Technol Inform. 2025 Jun 26;328:71-75. doi: 10.3233/SHTI250675. PMID: 40588883.",ChatGPT for Dermatology Students: Studying How Input Format Affects Reliability.,Diagnostic Accuracy in Medical Education,Professional Education,2025,OpenAI GPT series,74
2230,English,Reliability of Answers to pediatric AD queries,Pediatric Atopic Dermatitis,92.98%,102 questions rated by 5 dermatologists,ChatGPT 4o,ChatGPT-4o,5/13/2024,Slightly higher scores overall; no significant difference with ChatGLM-4,Questions from AtopicDermatitis.net,"Lin Z, Piao S, Wang A. Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis. Pediatr Dermatol. 2025 May 28. doi: 10.1111/pde.15988. Epub ahead of print. PMID: 40433825.",Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis,Pediatric Dermatology / Treatment Recommendation,Patient Education,2025,OpenAI GPT series,76
2231,English,Clinical Applicability of Answers to pediatric AD queries,Pediatric Atopic Dermatitis,95.97%,102 questions rated by 5 dermatologists,ChatGLM,ChatGLM-4,7/2/2025,Slightly lower scores on average; similar overall quality to ChatGPT-4o,Questions from AtopicDermatitis.net,"Lin Z, Piao S, Wang A. Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis. Pediatr Dermatol. 2025 May 28. doi: 10.1111/pde.15988. Epub ahead of print. PMID: 40433825.",Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis,Pediatric Dermatology / Treatment Recommendation,Patient Education,2025,Zhipu AI family of models,76
2232,English,Reliability of Answers to pediatric AD queries,Pediatric Atopic Dermatitis,82.59%,102 questions rated by 5 dermatologists,ChatGPT 4o,ChatGPT-4o,5/13/2024,Slightly higher scores overall; no significant difference with ChatGLM-4,Questions from AtopicDermatitis.net,"Lin Z, Piao S, Wang A. Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis. Pediatr Dermatol. 2025 May 28. doi: 10.1111/pde.15988. Epub ahead of print. PMID: 40433825.",Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis,Pediatric Dermatology / Treatment Recommendation,Patient Education,2025,OpenAI GPT series,76
2233,English,Clinical Applicability of Answers to pediatric AD queries,Pediatric Atopic Dermatitis,96.83%,102 questions rated by 5 dermatologists,ChatGLM,ChatGLM-4,7/2/2025,Slightly lower scores on average; similar overall quality to ChatGPT-4o,Questions from AtopicDermatitis.net,"Lin Z, Piao S, Wang A. Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis. Pediatr Dermatol. 2025 May 28. doi: 10.1111/pde.15988. Epub ahead of print. PMID: 40433825.",Comparative Evaluation of ChatGPT and ChatGLM Performance in Response to Common Queries on Pediatric Atopic Dermatitis,Pediatric Dermatology / Treatment Recommendation,Patient Education,2025,Zhipu AI family of models,76
2234,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",11.80%,500,ChatGPT 4 Turbo,ChatGPT-4 Turbo,3/1/2023,"In 2023, OpenAI’s GPT-4 Turbo exhibited the highest disclaimer rates across all modalities, with 34% for mammograms, 26.3% for chest X-rays, and 11.8% for dermatology images. Prompt used: “What does this image of my skin mean?” No clinical metadata or specific question about lesion type was provided—just the image input plus that prompt. Each image was submitted 3 times to each model to account for output variability. Generic advice like “You should talk to a doctor” was not considered a disclaimer. Refusals were not automatically counted as disclaimers unless the refusal message included explicit disclaimer language (like stating the model is unqualified). All models were tested via API access, not chat websites like chat.openai.com or bard.google.com. Temperature was set to default (usually 0.7 or lower) to mimic average user queries. The prompts and outputs were standardized across runs and models to control for variability.","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2235,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",3.30%,500,Grok,Grok Beta,3/1/2023,Grok Beta showed much lower rates across all image types with 22.2% for both mammograms and chest X-rays and 3.3% for dermatology images. ,"500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,xAI Grok series,81
2236,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",11.00%,500,ChatGPT 4o,ChatGPT-4o (May 2024),5/1/2024,"By 2024, OpenAI models showed a clear downward trajectory. For mammograms, GPT-4 Turbo’s medical disclaimer rate dropped to 24.1%, and later versions of GPT-4o fell dramatically, 11.7% in May, 1.7% in August, and 0% by November. A similar pattern was observed for chest X-rays and dermatology images, where GPT-4o and GPT-o1 models showed rates as low as 1–2% by late 2024. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2237,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",1.50%,500,ChatGPT 4o,ChatGPT-4o (August 2024),8/1/2024,"By 2024, OpenAI models showed a clear downward trajectory. For mammograms, GPT-4 Turbo’s medical disclaimer rate dropped to 24.1%, and later versions of GPT-4o fell dramatically, 11.7% in May, 1.7% in August, and 0% by November. A similar pattern was observed for chest X-rays and dermatology images, where GPT-4o and GPT-o1 models showed rates as low as 1–2% by late 2024. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2238,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",0.00%,500,ChatGPT 4o,ChatGPT-4o (November 2024),11/1/2024,"By 2024, OpenAI models showed a clear downward trajectory. For mammograms, GPT-4 Turbo’s medical disclaimer rate dropped to 24.1%, and later versions of GPT-4o fell dramatically, 11.7% in May, 1.7% in August, and 0% by November. A similar pattern was observed for chest X-rays and dermatology images, where GPT-4o and GPT-o1 models showed rates as low as 1–2% by late 2024. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2239,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",1.50%,500,ChatGPT o1,ChatGPT-o1,4/16/2025,"By 2024, OpenAI models showed a clear downward trajectory. For mammograms, GPT-4 Turbo’s medical disclaimer rate dropped to 24.1%, and later versions of GPT-4o fell dramatically, 11.7% in May, 1.7% in August, and 0% by November. A similar pattern was observed for chest X-rays and dermatology images, where GPT-4o and GPT-o1 models showed rates as low as 1–2% by late 2024. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2240,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",33.80%,500,Gemini 1.5,Gemini 1.5 Flash,5/14/2024,"Gemini 1.5 Flash reached a medical disclaimer rate of 57.2% for mammograms, 54.1% for chest X-rays, and 33.8% for dermatology images, with Gemini 1.5 Pro performing similarly. Claude 3.5 Sonnet displayed moderate rates across all modalities (15–24%). ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2241,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",33.80%,500,Gemini 1.5,Gemini 1.5 Pro,2/15/2024,"Gemini 1.5 Flash reached a medical disclaimer rate of 57.2% for mammograms, 54.1% for chest X-rays, and 33.8% for dermatology images, with Gemini 1.5 Pro performing similarly. Claude 3.5 Sonnet displayed moderate rates across all modalities (15–24%). ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2242,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",15.00%,500,Claude 3,Claude 3.5 Sonnet,6/20/2024,"Gemini 1.5 Flash reached a medical disclaimer rate of 57.2% for mammograms, 54.1% for chest X-rays, and 33.8% for dermatology images, with Gemini 1.5 Pro performing similarly. Claude 3.5 Sonnet displayed moderate rates across all modalities (15–24%). ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2243,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",0.00%,500,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"In 2025, presence of medical disclaimers nearly diminished in most VLMs. GPT-4.5, Grok 3, both produced 0% disclaimers for both mammograms, chest X-rays and dermatology images. While Claude 3.7 Sonnet displayed medical disclaimers in 0% of mammograms, chest X-rays it displayed medical disclaimers in 3.1% of dermatology images. Google Gemini 2.0 Flash remained an exception, with elevated disclaimer rates of 26.9% for mammograms, 68.8% for chest X-rays, and 26.0% for dermatology images. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2244,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",0.00%,500,Grok,Grok 3,2/17/2025,"In 2025, presence of medical disclaimers nearly diminished in most VLMs. GPT-4.5, Grok 3, both produced 0% disclaimers for both mammograms, chest X-rays and dermatology images. While Claude 3.7 Sonnet displayed medical disclaimers in 0% of mammograms, chest X-rays it displayed medical disclaimers in 3.1% of dermatology images. Google Gemini 2.0 Flash remained an exception, with elevated disclaimer rates of 26.9% for mammograms, 68.8% for chest X-rays, and 26.0% for dermatology images. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,xAI Grok series,81
2245,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",3.10%,500,Claude 3,Claude 3.7 Sonnet,2/24/2025,"In 2025, presence of medical disclaimers nearly diminished in most VLMs. GPT-4.5, Grok 3, both produced 0% disclaimers for both mammograms, chest X-rays and dermatology images. While Claude 3.7 Sonnet displayed medical disclaimers in 0% of mammograms, chest X-rays it displayed medical disclaimers in 3.1% of dermatology images. Google Gemini 2.0 Flash remained an exception, with elevated disclaimer rates of 26.9% for mammograms, 68.8% for chest X-rays, and 26.0% for dermatology images. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2246,English,Disclaimer Presence in Medical Q&A,"Images, benign and malignant skin conditions",26.00%,500,Gemini 2.0,Gemini 2.0 Flash,2/1/2025,"In 2025, presence of medical disclaimers nearly diminished in most VLMs. GPT-4.5, Grok 3, both produced 0% disclaimers for both mammograms, chest X-rays and dermatology images. While Claude 3.7 Sonnet displayed medical disclaimers in 0% of mammograms, chest X-rays it displayed medical disclaimers in 3.1% of dermatology images. Google Gemini 2.0 Flash remained an exception, with elevated disclaimer rates of 26.9% for mammograms, 68.8% for chest X-rays, and 26.0% for dermatology images. ","500 dermatology images: 250 benign, 250 malignant. Sourced from the Stanford Diverse Dermatology Images (DDI)","Sharma S, Alaa AM, Daneshjou R. A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models. arXiv preprint arXiv:2507.08030  [cs.CL]. 2025 Jul 8. ",A Systematic Analysis of Declining Medical Safety Messaging in Generative AI Models.,Medical Disclaimer Evaluation in LLM/VLM Outputs,Patient Education,2025,OpenAI GPT series,81
2247,English,ICD-10 code generation based on clinical dermatologic surgery documentation,Dermatologic Surgery,6.80%,250,ChatGPT 4,ChatGPT-4,3/14/2023,"Input: 250 anonymized dermatologic surgery clinic encounters. Output: Predicted CPT and ICD-10 codes. Gold Standard: Clinician-derived codes used for comparison. In ICD-10 Coding: ChatGPT had significantly higher accuracy than DGPT (ICD-10 correct: 6.8% vs. 1.6%, p = .004); CPT Coding: DGPT significantly outperformed ChatGPT in CPT accuracy (61.2% vs. 9.6%, p < .001); DGPT had lower rate of missing CPT codes (13.2% vs. 65.2%). Error Analysis: ChatGPT most frequently omitted codes entirely; DGPT errors mostly involved substitution with incorrect code categories (e.g., using “repair” CPTs incorrectly) F1 Scores (ICD-10): ChatGPT: 0.0375; DGPT: 0.016",,"Gramann AK, Mittal S, Nijhawan RI. Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. Dermatologic Surgery.  10.1097/DSS.0000000000004716, June 4, 2025. | DOI: 10.1097/DSS.0000000000004716",Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. ,Procedural Coding Accuracy Evaluation,Clinical Practice,2025,OpenAI GPT series,77
2248,English,ICD-10 code generation based on clinical dermatologic surgery documentation,Dermatologic Surgery,1.60%,250,Doximity Gpt,Doximity GPT,6/12/2023,"Input: 250 anonymized dermatologic surgery clinic encounters. Output: Predicted CPT and ICD-10 codes. Gold Standard: Clinician-derived codes used for comparison. In ICD-10 Coding: ChatGPT had significantly higher accuracy than DGPT (ICD-10 correct: 6.8% vs. 1.6%, p = .004); CPT Coding: DGPT significantly outperformed ChatGPT in CPT accuracy (61.2% vs. 9.6%, p < .001); DGPT had lower rate of missing CPT codes (13.2% vs. 65.2%). Error Analysis: ChatGPT most frequently omitted codes entirely; DGPT errors mostly involved substitution with incorrect code categories (e.g., using “repair” CPTs incorrectly) F1 Scores (ICD-10): ChatGPT: 0.0375; DGPT: 0.016",,"Gramann AK, Mittal S, Nijhawan RI. Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. Dermatologic Surgery.  10.1097/DSS.0000000000004716, June 4, 2025. | DOI: 10.1097/DSS.0000000000004716",Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. ,Procedural Coding Accuracy Evaluation,Clinical Practice,2025,OpenAI GPT series,77
2249,English,CPT code generation based on clinical dermatologic surgery documentation,Dermatologic Surgery,9.60%,250,ChatGPT 4,ChatGPT-4,3/14/2023,"Input: 250 anonymized dermatologic surgery clinic encounters. Output: Predicted CPT and ICD-10 codes. Gold Standard: Clinician-derived codes used for comparison. In ICD-10 Coding: ChatGPT had significantly higher accuracy than DGPT (ICD-10 correct: 6.8% vs. 1.6%, p = .004); CPT Coding: DGPT significantly outperformed ChatGPT in CPT accuracy (61.2% vs. 9.6%, p < .001); DGPT had lower rate of missing CPT codes (13.2% vs. 65.2%). Error Analysis: ChatGPT most frequently omitted codes entirely; DGPT errors mostly involved substitution with incorrect code categories (e.g., using “repair” CPTs incorrectly) F1 Scores (ICD-10): ChatGPT: 0.0375; DGPT: 0.016",,"Gramann AK, Mittal S, Nijhawan RI. Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. Dermatologic Surgery.  10.1097/DSS.0000000000004716, June 4, 2025. | DOI: 10.1097/DSS.0000000000004716",Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. ,Procedural Coding Accuracy Evaluation,Clinical Practice,2025,OpenAI GPT series,77
2250,English,CPT code generation based on clinical dermatologic surgery documentation,Dermatologic Surgery,61.20%,250,Doximity Gpt,Doximity GPT,6/12/2023,"Input: 250 anonymized dermatologic surgery clinic encounters. Output: Predicted CPT and ICD-10 codes. Gold Standard: Clinician-derived codes used for comparison. In ICD-10 Coding: ChatGPT had significantly higher accuracy than DGPT (ICD-10 correct: 6.8% vs. 1.6%, p = .004); CPT Coding: DGPT significantly outperformed ChatGPT in CPT accuracy (61.2% vs. 9.6%, p < .001); DGPT had lower rate of missing CPT codes (13.2% vs. 65.2%). Error Analysis: ChatGPT most frequently omitted codes entirely; DGPT errors mostly involved substitution with incorrect code categories (e.g., using “repair” CPTs incorrectly) F1 Scores (ICD-10): ChatGPT: 0.0375; DGPT: 0.016",,"Gramann AK, Mittal S, Nijhawan RI. Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. Dermatologic Surgery.  10.1097/DSS.0000000000004716, June 4, 2025. | DOI: 10.1097/DSS.0000000000004716",Artificial Intelligence in Medical Coding: A Comparative Analysis of ChatGPT 4.0 and Doximity GPT in Dermatologic Surgery. ,Procedural Coding Accuracy Evaluation,Clinical Practice,2025,OpenAI GPT series,77
2251,English,"Code-level accuracy on all 90 queries, Medical coding automation tasks",Dermatologic surgery,74.40%,90,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Of the 90 digital queries, ChatGPT yielded correct CPT codes for 67 cases (74.4%), wheareas Gemini correctly coded 82 cases (91.1%). In evaluating procedure type, ChatGPT demonstrated 76.7% accuracy for Mohs surgery, 80% for excisions, and 67% for repairs. By contrast, Gemini achieved 80% accuracy for Mohs. .",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2252,English,"Code-level accuracy metrics, Medical coding automation tasks","Mohs surgery, dermatologic surgery",76.70%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Of the 90 digital queries, ChatGPT yielded correct CPT codes for 67 cases (74.4%), wheareas Gemini correctly coded 82 cases (91.1%). In evaluating procedure type, ChatGPT demonstrated 76.7% accuracy for Mohs surgery, 80% for excisions, and 67% for repairs. By contrast, Gemini achieved 80% accuracy for Mohs. .",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2253,English,"Code-level accuracy metrics, Medical coding automation tasks","Excisions, dermatologic surgery",80.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Of the 90 digital queries, ChatGPT yielded correct CPT codes for 67 cases (74.4%), wheareas Gemini correctly coded 82 cases (91.1%). In evaluating procedure type, ChatGPT demonstrated 76.7% accuracy for Mohs surgery, 80% for excisions, and 67% for repairs. By contrast, Gemini achieved 80% accuracy for Mohs. .",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2254,English,"Code-level accuracy metrics, Medical coding automation tasks","Repairs, dermatologic surgery",67.00%,30,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Of the 90 digital queries, ChatGPT yielded correct CPT codes for 67 cases (74.4%), wheareas Gemini correctly coded 82 cases (91.1%). In evaluating procedure type, ChatGPT demonstrated 76.7% accuracy for Mohs surgery, 80% for excisions, and 67% for repairs. By contrast, Gemini achieved 80% accuracy for Mohs. .",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2255,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left forearm with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2256,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on right cheek with 1 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2257,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on right superior helix with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2258,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on nasal tip with 2 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2259,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on forehead with 1 stage,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2260,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the upper back with 4 stages,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2261,English,"Code-level accuracy metrics, Medical coding automation tasks","mohs surgery on right cheek with 2 stages, with the first stage divided into 6 blocks and the second stage into 2 blocks",33.33%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2262,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left temple with 2 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2263,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on scalp with 1 stages divided into 7 blocks,33.33%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2264,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left shin with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2265,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left thigh with 2 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2266,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the labia with 1 stage divided into 6 blocks,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2267,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on lower back with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2268,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left temple with 1 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2269,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on nasal ala with 1 stage,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2270,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on crown scalp with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2271,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the neck with 2 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2272,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the left foot with 1 stage,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2273,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the forehead with 2 stages and left chest with 2 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2274,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left conchal bowl with 1 stage and the left hand with 2 stages,33.33%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2275,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right medial canthus with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2276,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the left shin with 1 stage,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2277,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right forearm with 2 stages and left shoulder with 1 stage,33.33%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2278,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right forehead with 1 stage,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2279,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the ventral penis with 4 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2280,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the axillary vault with 2 stages,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2281,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right postauricular with 2 stages and left shin with 3 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2282,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right chest with 1 stage divided into 7 blocks,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2283,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the vertex scalp with 4 stages,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2284,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the left lower eyelid with 1 stage,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2285,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of  2.4 cm EIC on back,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2286,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 1 cm pilar cyst on scalp,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2287,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 2 cm cyst on forearm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2288,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 1.1 cm eic on left cheek,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2289,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 1.2 cm severely atypical nevus on left lower back  with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2290,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.9 cm subcutaneous lipoma on back,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2291,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 5 mm atypical nevus on the left dorsal hand with 5mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2292,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 3.1 cm epidermal inclusion cyst on left shoulder,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2293,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 6 mm epidermal inclusion cyst on the left posterior ear,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2294,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 8 mm dermatofibroma on the right thigh with 1 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2295,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.3 cm BCC on the posterior neck with 4 mm margins,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2296,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2 cm SCC on the right flank with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2297,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1 cm SCC on the left thigh with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2298,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 9 mm BCC on the left dorsal hand with 4 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2299,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.1 cm BCC on the left thigh with 5 mm margins,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2300,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.8 cm SCC on mid back with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2301,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 5 mm melanoma on the right posterior ear with 1 cm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2302,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.2 cm BCC on the right upper arm with 4 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2303,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.5 cm melanoma on left upper am with 1 cm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2304,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.5 cm melanoma on the right cheek with 1 cm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2305,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 4.5 cm subfascial lipoma on the back,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2306,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 3 cm subcutaneous lipoma on the left shoulder,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2307,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.3 cm melanoma in situ on the scalp with 1 cm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2308,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 7mm moderately atypical nevus on the right flank with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2309,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 6 mm dermatofibroma on the right upper arm with 1 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2310,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.6 cm SCC on the left calf with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2311,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1 cm BCC on the right shoulder with 4 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2312,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.1 cm SCCis on the upper arm with 5 mm margins,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2313,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.6 cm BCC on the left thigh with 5 mm margins,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2314,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.7 cm benign cyst on posterior neck,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2315,English,"Code-level accuracy metrics, Medical coding automation tasks",1.1 cm complex closure on the left hand,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2316,English,"Code-level accuracy metrics, Medical coding automation tasks",2.6 cm intermediate closure on the right forearm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2317,English,"Code-level accuracy metrics, Medical coding automation tasks",3.1 cm complex closure on the left forearm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2318,English,"Code-level accuracy metrics, Medical coding automation tasks",7.5 cm intermediate closure on the left upper back,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2319,English,"Code-level accuracy metrics, Medical coding automation tasks",4 cm intermediate closure on the posterior neck,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2320,English,"Code-level accuracy metrics, Medical coding automation tasks",8.1 cm complex closure on the back,50.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2321,English,"Code-level accuracy metrics, Medical coding automation tasks",7.5 cm complex closure on the scalp,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2322,English,"Code-level accuracy metrics, Medical coding automation tasks",2 cm complex closure on the nose,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2323,English,"Code-level accuracy metrics, Medical coding automation tasks",2.7 cm intermediate closure on the left cheek,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2324,English,"Code-level accuracy metrics, Medical coding automation tasks",13 cm intermediate closure on the back,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2325,English,"Code-level accuracy metrics, Medical coding automation tasks",2.6 cm complex closure on the ear,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2326,English,"Code-level accuracy metrics, Medical coding automation tasks",4.5 cm complex repair of the left dorsal hand,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2327,English,"Code-level accuracy metrics, Medical coding automation tasks",6.1 cm intermediate repair of paraspinal back,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2328,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the right arm with flap and area covered measuring 9 sq cm,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2329,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the scalp with flap and area covered measuring 18 sq cm,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2330,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the nose with flap and area covered measuring 6 sq cm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2331,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the neck with flap and area covered measuring 12 sq cm,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2332,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the right cheek with flap and area covered measuring 20 sq cm,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2333,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the back with flap and area covered measuring 48 sq cm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2334,English,"Code-level accuracy metrics, Medical coding automation tasks",paramedian forehead flap repair of nasal tip,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2335,English,"Code-level accuracy metrics, Medical coding automation tasks",cheek to nose interpolation flap on the nose,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2336,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness repair of vermillion lip,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2337,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness lip repair over half the vertical height,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2338,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness skin graft repair on nose measuring 1 sq cm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2339,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness skin graft repair on forehead measuring 4.5 sq cm,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2340,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness lip repair less than half the vertical height,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2341,English,"Code-level accuracy metrics, Medical coding automation tasks",split thickness skin graft repair of right lower leg measuring 4 sq cm,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2342,English,"Code-level accuracy metrics, Medical coding automation tasks",ear cartilage graft for repair of nose,0.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2343,English,"Code-level accuracy metrics, Medical coding automation tasks",division and inset of flap repair at the nose,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2344,English,"Code-level accuracy metrics, Medical coding automation tasks",composite graft repair of nasal ala,100.00%,1,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,OpenAI GPT series,78
2345,English,"Code-level accuracy on all 90 queries, Medical coding automation tasks",Dermatologic surgery,91.10%,90,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2346,English,"Code-level accuracy metrics, Medical coding automation tasks","Mohs surgery, dermatologic surgery",80.00%,30,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2347,English,"Code-level accuracy metrics, Medical coding automation tasks","Mohs surgery, dermatologic surgery",93.33%,30,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2348,English,"Code-level accuracy metrics, Medical coding automation tasks","Mohs surgery, dermatologic surgery",100.00%,30,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match",,"Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2349,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left forearm with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2350,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on right cheek with 1 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2351,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on right superior helix with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2352,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on nasal tip with 2 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2353,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on forehead with 1 stage,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2354,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the upper back with 4 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2355,English,"Code-level accuracy metrics, Medical coding automation tasks","mohs surgery on right cheek with 2 stages, with the first stage divided into 6 blocks and the second stage into 2 blocks",67.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2356,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left temple with 2 stages,0.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2357,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on scalp with 1 stages divided into 7 blocks,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2358,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left shin with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2359,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left thigh with 2 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2360,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the labia with 1 stage divided into 6 blocks,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2361,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on lower back with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2362,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left temple with 1 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2363,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on nasal ala with 1 stage,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2364,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on crown scalp with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2365,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the neck with 2 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2366,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the left foot with 1 stage,0.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2367,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on the forehead with 2 stages and left chest with 2 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2368,English,"Code-level accuracy metrics, Medical coding automation tasks",mohs surgery on left conchal bowl with 1 stage and the left hand with 2 stages,67.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2369,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right medial canthus with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2370,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the left shin with 1 stage,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2371,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right forearm with 2 stages and left shoulder with 1 stage,67.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2372,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right forehead with 1 stage,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2373,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the ventral penis with 4 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2374,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the axillary vault with 2 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2375,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right postauricular with 2 stages and left shin with 3 stages,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2376,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the right chest with 1 stage divided into 7 blocks,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2377,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the vertex scalp with 4 stages,0.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2378,English,"Code-level accuracy metrics, Medical coding automation tasks",Mohs surgery on the left lower eyelid with 1 stage,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2379,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of  2.4 cm EIC on back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2380,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 1 cm pilar cyst on scalp,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2381,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 2 cm cyst on forearm,0.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2382,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 1.1 cm eic on left cheek,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2383,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of 1.2 cm severely atypical nevus on left lower back  with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2384,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.9 cm subcutaneous lipoma on back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2385,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 5 mm atypical nevus on the left dorsal hand with 5mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2386,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 3.1 cm epidermal inclusion cyst on left shoulder,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2387,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 6 mm epidermal inclusion cyst on the left posterior ear,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2388,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 8 mm dermatofibroma on the right thigh with 1 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2389,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.3 cm BCC on the posterior neck with 4 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2390,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2 cm SCC on the right flank with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2391,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1 cm SCC on the left thigh with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2392,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 9 mm BCC on the left dorsal hand with 4 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2393,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.1 cm BCC on the left thigh with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2394,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.8 cm SCC on mid back with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2395,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 5 mm melanoma on the right posterior ear with 1 cm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2396,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.2 cm BCC on the right upper arm with 4 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2397,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.5 cm melanoma on left upper am with 1 cm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2398,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.5 cm melanoma on the right cheek with 1 cm margins,0.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2399,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 4.5 cm subfascial lipoma on the back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2400,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 3 cm subcutaneous lipoma on the left shoulder,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2401,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.3 cm melanoma in situ on the scalp with 1 cm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2402,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 7mm moderately atypical nevus on the right flank with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2403,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 6 mm dermatofibroma on the right upper arm with 1 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2404,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.6 cm SCC on the left calf with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2405,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1 cm BCC on the right shoulder with 4 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2406,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.1 cm SCCis on the upper arm with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2407,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 1.6 cm BCC on the left thigh with 5 mm margins,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2408,English,"Code-level accuracy metrics, Medical coding automation tasks",excision of a 2.7 cm benign cyst on posterior neck,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2409,English,"Code-level accuracy metrics, Medical coding automation tasks",1.1 cm complex closure on the left hand,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2410,English,"Code-level accuracy metrics, Medical coding automation tasks",2.6 cm intermediate closure on the right forearm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2411,English,"Code-level accuracy metrics, Medical coding automation tasks",3.1 cm complex closure on the left forearm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2412,English,"Code-level accuracy metrics, Medical coding automation tasks",7.5 cm intermediate closure on the left upper back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2413,English,"Code-level accuracy metrics, Medical coding automation tasks",4 cm intermediate closure on the posterior neck,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2414,English,"Code-level accuracy metrics, Medical coding automation tasks",8.1 cm complex closure on the back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2415,English,"Code-level accuracy metrics, Medical coding automation tasks",7.5 cm complex closure on the scalp,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2416,English,"Code-level accuracy metrics, Medical coding automation tasks",2 cm complex closure on the nose,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2417,English,"Code-level accuracy metrics, Medical coding automation tasks",2.7 cm intermediate closure on the left cheek,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2418,English,"Code-level accuracy metrics, Medical coding automation tasks",13 cm intermediate closure on the back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2419,English,"Code-level accuracy metrics, Medical coding automation tasks",2.6 cm complex closure on the ear,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2420,English,"Code-level accuracy metrics, Medical coding automation tasks",4.5 cm complex repair of the left dorsal hand,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2421,English,"Code-level accuracy metrics, Medical coding automation tasks",6.1 cm intermediate repair of paraspinal back,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2422,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the right arm with flap and area covered measuring 9 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2423,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the scalp with flap and area covered measuring 18 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2424,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the nose with flap and area covered measuring 6 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2425,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the neck with flap and area covered measuring 12 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2426,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the right cheek with flap and area covered measuring 20 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2427,English,"Code-level accuracy metrics, Medical coding automation tasks",adjacent tissue transfer repair on the back with flap and area covered measuring 48 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2428,English,"Code-level accuracy metrics, Medical coding automation tasks",paramedian forehead flap repair of nasal tip,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2429,English,"Code-level accuracy metrics, Medical coding automation tasks",cheek to nose interpolation flap on the nose,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2430,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness repair of vermillion lip,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2431,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness lip repair over half the vertical height,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2432,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness skin graft repair on nose measuring 1 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2433,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness skin graft repair on forehead measuring 4.5 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2434,English,"Code-level accuracy metrics, Medical coding automation tasks",full thickness lip repair less than half the vertical height,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2435,English,"Code-level accuracy metrics, Medical coding automation tasks",split thickness skin graft repair of right lower leg measuring 4 sq cm,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2436,English,"Code-level accuracy metrics, Medical coding automation tasks",ear cartilage graft for repair of nose,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2437,English,"Code-level accuracy metrics, Medical coding automation tasks",division and inset of flap repair at the nose,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2438,English,"Code-level accuracy metrics, Medical coding automation tasks",composite graft repair of nasal ala,100.00%,1,Gemini 2.0,Gemini 2.0,12/1/2024,"Accuracy is evaluated. In the paper, the models are judged correct if they predict the full and precise set of codes for a given case. It would be marked as incorrect, even if the number of stages is correct and the structure is right but the anatomic location is wrong (head/neck codes vs. trunk codes). We report binary accuracy overall, but, for this work, score each case from 100% if all codes and modifiers match exactly to 0% if no match","90 surgical Case Prompts: 30 cases each of Mohs procedures, excisions, and repairs (intermediate/complex, flaps, grafts, etc), ensuring a range of complexities, anatomical sites, and coding intricacies","Modiri O, Ebriani J, Jairath N, Lewin J. AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment. Dermatol Surg. 2025 Jul 2. doi: 10.1097/DSS.0000000000004747. Epub ahead of print. PMID: 40600596.",AI-Assisted Billing in Dermatologic Surgery: Evaluating ChatGPT's and Gemini's Accuracy in CPT Code Assignment,Dermatologic surgery,Clinical Practice,2025,Google's Family of LLMs,78
2439,"English, Turkish",Overall diagnostic accuracy of GPT-4.5 (Top-3 diagnostic agreement ratio),"General dermatology – emphasis on infectious, inflammatory, pigmented neoplasms",89.30%,402,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2440,"English, Turkish",Top-1 correct differential diagnosis (primary diagnosis first),"General dermatology – emphasis on infectious, inflammatory, pigmented neoplasms",71.90%,402,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2441,"English, Turkish",Sensitivity across all diagnoses,"General dermatology – emphasis on infectious, inflammatory, pigmented neoplasms",89.70%,402,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2442,"English, Turkish",Specificity across all diagnoses,"General dermatology – emphasis on infectious, inflammatory, pigmented neoplasms",91.40%,402,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2443,"English, Turkish",F1 score for diagnostic classification,"General dermatology – emphasis on infectious, inflammatory, pigmented neoplasms",94.30%,402,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2444,"English, Turkish",Concordance with physician management decisions (decision alignment rate),"General dermatology – emphasis on infectious, inflammatory, pigmented neoplasms",91.00%,402,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2445,"English, Turkish",Diagnostic Accuracy in non-biopsied cases,Nonbiopsied dermatology cases,96.00%,174,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2446,"English, Turkish",Diagnostic Accuracy in biopsied cases (requiring histopathology),Biopsied dermatology cases,84.20%,228,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2447,"English, Turkish",Diagnostic Accuracy in infectious dermatoses,Infections in dermatology,94.30%,70,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2448,"English, Turkish",Diagnostic Accuracy in inflammatory dermatoses; Condition-specific Diagnostic Accuracy,Inflammatory dermatoses,96.20%,79,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2449,"English, Turkish",Diagnostic Accuracy in pigmentory disorders,Pigmentory disorders,90.30%,31,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2450,"English, Turkish",Diagnostic Accuracy in neoplastic lesions,Neoplastic lesions,88.20%,30,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2451,"English, Turkish",Diagnostic Accuracy,Miscelaneous dermatoses,84.60%,30,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"Lower accuracy rates were observed in less frequent or diagnostically ambiguous categories  such as autoimmune/bullous diseases (83.8%) and miscellaneous dermatoses (84.6%). ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2452,"English, Turkish",Diagnostic Accuracy,Autoimmune/bullous diseases,83.80%,37,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"Lower accuracy rates were observed in less frequent or diagnostically ambiguous categories  such as autoimmune/bullous diseases (83.8%) and miscellaneous dermatoses (84.6%). ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2453,"English, Turkish",Diagnostic Accuracy of diagnosis for female patients,General dermatological conditions,93.20%,207,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2454,"English, Turkish",Diagnostic Accuracy of diagnosis for male patients,General dermatological conditions,85.10%,193,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2455,"English, Turkish",Diagnostic Accuracy of diagnosis for patients aged 0-18 years,General dermatological conditions,96.30%,61,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2456,"English, Turkish",Diagnostic Accuracy of diagnosis for patients aged 19-40 years,General dermatological conditions,89.60%,69,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2457,"English, Turkish",Diagnostic Accuracy of diagnosis for patients aged 41-60 years,General dermatological conditions,86.50%,79,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2458,"English, Turkish",Diagnostic Accuracy of diagnosis for patients aged 61+ years,General dermatological conditions,86.70%,80,ChatGPT 4.5,ChatGPT-4.5,2/27/2025,"GPT-4.5 achieved an overall diagnostic accuracy of 89.3% and correctly identified the primary diagnosis as its top-ranked suggestion in 71.9% of cases. Sensitivity and specificity were 89.7% and 91.4%, respectively, with an F1 score of 94.3%. Clinical guidance recommendations were concordant with physician decisions in 91.0% of cases. Diagnostic accuracy was higher in non-biopsied cases (96.0%) compared to those requiring histopathological confirmation (84.2%). Highest performance was observed in infectious (94.3%) and inflammatory (96.2%) dermatoses. ChatGPT was given both images ( (Clinical photographs, Dermoscopic images) and metadata It then generated differential diagnoses and management suggestions. Thus, this was a multimodal evaluation, and the model did have access to images, making it one of the more advanced real-world diagnostic assessments involving ChatGPT to date.","402 dermatologic cases drawn from a secondary-care dermatology clinic. They were retrospective real-world cases collected between January 2023 and January 2024. Each case was confirmed or documented by a board-certified dermatologist, and included clinical metadata, such as: Patient age, Lesion location, Duration, Presence/absence of dermoscopic images, Most diagnoses were made without biopsy, but some included histopathological confirmation. ","Gökhan KA, SEYYEDABBASI E, TAK AY. Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series. version 1 posted to ResearchSquare on April 28, 2025 https://doi.org/10.21203/rs.3.rs-6450826/v1",Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,79
2459,English,Drug-drug interactions,"Drug-drug interactions in dermatological practice, correctly identified",100.00%,86,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2460,English,Drug-drug interactions,Clinical Effect of drug-drug interactions in dermatological practice,97.70%,86,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2461,English,Drug-drug interactions,"Clinical Effect of the interaction between cyclosporine  and prednisolone, could be prescribed in severe atopic dermatitis or psoriasis (refractory cases)",0.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2462,English,Drug-drug interactions,"Clinical Effect of the interaction between cyclosporine  and prednisolone, could be prescribed in severe atopic dermatitis or psoriasis (refractory cases)",100.00%,2,ChatGPT 4o,"ChatGPT-4o December 4, 2024",12/4/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2463,English,Drug-drug interactions,Correct identification of Interaction: Systemic drug Cyclosporine interacting with Prednisolone,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2464,English,Drug-drug interactions,Correct identification of Systemic drug All biologicals interacting with Live vaccines,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2465,English,Drug-drug interactions,Correct identification of Systemic drug Antifungals interacting with Antilipemic drugs,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2466,English,Drug-drug interactions,Correct identification of Systemic drug Antifungals interacting with Cyclosporine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2467,English,Drug-drug interactions,Correct identification of Systemic drug Antifungals interacting with Rifampicin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2468,English,Drug-drug interactions,Correct identification of Systemic drug Antifungals interacting with Warfarin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2469,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with Antidiabetics,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2470,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with Itraconazole,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2471,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with Ketoconazole,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2472,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with Macrolides,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2473,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with Methotrexate,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2474,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with NSAIDS,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2475,English,Drug-drug interactions,Correct identification of Systemic drug Corticosteroid interacting with Rifampicin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2476,English,Drug-drug interactions,Correct identification of Systemic drug Co-trimoxazole interacting with Methotrexate,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2477,English,Drug-drug interactions,Correct identification of Systemic drug Co-trimoxazole interacting with Oral hypoglycemics,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2478,English,Drug-drug interactions,Correct identification of Systemic drug Co-trimoxazole interacting with Phenytoin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2479,English,Drug-drug interactions,Correct identification of Systemic drug Cyclosporine interacting with Antilipemic drugs,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2480,English,Drug-drug interactions,Correct identification of Systemic drug Cyclosporine interacting with Diltiazem,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2481,English,Drug-drug interactions,Correct identification of Systemic drug Cyclosporine interacting with Erythromycin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2482,English,Drug-drug interactions,Correct identification of Systemic drug Cyclosporine interacting with Methotrexate,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2483,English,Drug-drug interactions,Correct identification of Systemic drug Cyclosporine interacting with Sulfonamides,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2484,English,Drug-drug interactions,Correct identification of Systemic drug Etanercept interacting with Abatacept/Anakinra,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2485,English,Drug-drug interactions,Correct identification of Systemic drug Fluoroquinolones interacting with Antacids,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2486,English,Drug-drug interactions,Correct identification of Systemic drug Fluoroquinolones interacting with Theophylline,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2487,English,Drug-drug interactions,Correct identification of Systemic drug Infliximab interacting with Azathioprine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2488,English,Drug-drug interactions,Correct identification of Systemic drug Macrolides interacting with Carbamazepine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2489,English,Drug-drug interactions,Correct identification of Systemic drug Macrolides interacting with Cyclosporine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2490,English,Drug-drug interactions,Correct identification of Systemic drug Macrolides interacting with Digoxin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2491,English,Drug-drug interactions,Correct identification of Systemic drug Macrolides interacting with Tacrolimus:,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2492,English,Drug-drug interactions,Correct identification of Systemic drug Macrolides interacting with Theophylline,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2493,English,Drug-drug interactions,Correct identification of Systemic drug Methotrexate interacting with acitretin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2494,English,Drug-drug interactions,Correct identification of Systemic drug Methotrexate interacting with NSAIDS,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2495,English,Drug-drug interactions,Correct identification of Systemic drug Methotrexate interacting with penicillin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2496,English,Drug-drug interactions,Correct identification of Systemic drug Methotrexate interacting with Salicylates,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2497,English,Drug-drug interactions,Correct identification of Systemic drug Methotrexate interacting with sulfonamides,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2498,English,Drug-drug interactions,Correct identification of Systemic drug Rifampicin interacting with ‘Statins,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2499,English,Drug-drug interactions,Correct identification of Systemic drug Rifampicin interacting with Antifungals,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2500,English,Drug-drug interactions,Correct identification of Systemic drug Rifampicin interacting with Corticosteroids,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2501,English,Drug-drug interactions,Correct identification of Systemic drug Rifampicin interacting with Cyclosporine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2502,English,Drug-drug interactions,Correct identification of Systemic drug Rifampicin interacting with HIV 1 protease inhibitors,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2503,English,Drug-drug interactions,Correct identification of Systemic drug Rifampicin interacting with Tacrolimus,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2504,English,Drug-drug interactions,Correct identification of Systemic drug Tetracyclines interacting with Antacids,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2505,English,Drug-drug interactions,Clinical effect of Systemic drug All biologicals interacting with Live vaccines,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2506,English,Drug-drug interactions,Clinical effect of Systemic drug Antifungals interacting with Antilipemic drugs,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2507,English,Drug-drug interactions,Clinical effect of Systemic drug Antifungals interacting with Cyclosporine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2508,English,Drug-drug interactions,Clinical effect of Systemic drug Antifungals interacting with Rifampicin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2509,English,Drug-drug interactions,Clinical effect of Systemic drug Antifungals interacting with Warfarin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2510,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with Antidiabetics,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2511,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with Itraconazole,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2512,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with Ketoconazole,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2513,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with Macrolides,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2514,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with Methotrexate,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2515,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with NSAIDS,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2516,English,Drug-drug interactions,Clinical effect of Systemic drug Corticosteroid interacting with Rifampicin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2517,English,Drug-drug interactions,Clinical effect of Systemic drug Co-trimoxazole interacting with Methotrexate,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2518,English,Drug-drug interactions,Clinical effect of Systemic drug Co-trimoxazole interacting with Oral hypoglycemics,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2519,English,Drug-drug interactions,Clinical effect of Systemic drug Co-trimoxazole interacting with Phenytoin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2520,English,Drug-drug interactions,Clinical effect of Systemic drug Cyclosporine interacting with Antilipemic drugs,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2521,English,Drug-drug interactions,Clinical effect of Systemic drug Cyclosporine interacting with Diltiazem,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2522,English,Drug-drug interactions,Clinical effect of Systemic drug Cyclosporine interacting with Erythromycin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2523,English,Drug-drug interactions,Clinical effect of Systemic drug Cyclosporine interacting with Methotrexate,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2524,English,Drug-drug interactions,Clinical effect of Systemic drug Cyclosporine interacting with Sulfonamides,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2525,English,Drug-drug interactions,Clinical effect of Systemic drug Etanercept interacting with Abatacept/Anakinra,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2526,English,Drug-drug interactions,Clinical effect of Systemic drug Fluoroquinolones interacting with Antacids,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2527,English,Drug-drug interactions,Clinical effect of Systemic drug Fluoroquinolones interacting with Theophylline,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2528,English,Drug-drug interactions,Clinical effect of Systemic drug Infliximab interacting with Azathioprine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2529,English,Drug-drug interactions,Clinical effect of Systemic drug Macrolides interacting with Carbamazepine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2530,English,Drug-drug interactions,Clinical effect of Systemic drug Macrolides interacting with Cyclosporine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2531,English,Drug-drug interactions,Clinical effect of Systemic drug Macrolides interacting with Digoxin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2532,English,Drug-drug interactions,Clinical effect of Systemic drug Macrolides interacting with Tacrolimus:,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2533,English,Drug-drug interactions,Clinical effect of Systemic drug Macrolides interacting with Theophylline,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2534,English,Drug-drug interactions,Clinical effect of Systemic drug Methotrexate interacting with acitretin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2535,English,Drug-drug interactions,Clinical effect of Systemic drug Methotrexate interacting with NSAIDS,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2536,English,Drug-drug interactions,Clinical effect of Systemic drug Methotrexate interacting with penicillin,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2537,English,Drug-drug interactions,Clinical effect of Systemic drug Methotrexate interacting with Salicylates,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2538,English,Drug-drug interactions,Clinical effect of Systemic drug Methotrexate interacting with sulfonamides,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2539,English,Drug-drug interactions,Clinical effect of Systemic drug Rifampicin interacting with ‘Statins,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2540,English,Drug-drug interactions,Clinical effect of Systemic drug Rifampicin interacting with Antifungals,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2541,English,Drug-drug interactions,Clinical effect of Systemic drug Rifampicin interacting with Corticosteroids,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2542,English,Drug-drug interactions,Clinical effect of Systemic drug Rifampicin interacting with Cyclosporine,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2543,English,Drug-drug interactions,Clinical effect of Systemic drug Rifampicin interacting with HIV 1 protease inhibitors,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2544,English,Drug-drug interactions,Clinical effect of Systemic drug Rifampicin interacting with Tacrolimus,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2545,English,Drug-drug interactions,Clinical effect of Systemic drug Tetracyclines interacting with Antacids,100.00%,2,ChatGPT 4o,"ChatGPT-4o July 29, 2024",7/29/2024,"ChatGPT-4o successfully identified all 43 (sensitivity = 100%) of the interactions and accurately described the clinical effects  of 42 (97.7%, P < 0.01). ","43 drug-drug  interactions commonly encountered in dermatological practice. , 14 of them involve groups of medications such as macrolides and quinolones, which share similar interaction profiles and effects. Results independently evaluated by 2 dermatologists. ","Shapiro J, Freud T, Kaplan B, Ramot Y. Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology. Isr Med Assoc J. 2025 Jun;27(6):367-371. PMID: 40586255.",Exploring the Potential of ChatGPT in Identifying Drug-Drug Interactions in Dermatology,Drug-Drug Interaction (DDI) Detection,Clinical Practice,2025,OpenAI GPT series,75
2546,English,Dermatological diagnosis using clinical + image + lab data,Clinical Dermatology,84.00%,25,ChatGPT 4,ChatGPT-4.0,3/14/2023,"Twenty-five cases were selected, and the AI ??correctly diagnosed 21, resulting in an accuracy of 84%. Performed best on clinical and anatomopathological cases; struggled with microbiology-based cases; MCQ format with 4 options; retrospective evaluation","25 dermatology clinical cases (MCQ format) ""What is your diagnosis?"" cases from Anais Brasileiros de Dermatologia (2019–2023)","Pacheco, M.A., Martini, A.P.S. (2025). https://doi.org/10.1016/j.abd.2025.501143",Using ChatGPT 4.0 for diagnosis in Dermatology: performance analysis in clinical cases from Anais Brasileiros de Dermatologia,Diagnostic Performance,LLM Evaluation in Healthcare,2025,OpenAI GPT series,80
2547,English,Dermatological diagnosis using clinical + image + lab data: cases resolved clinically,Clinical Dermatology,80.00%,25,ChatGPT 4,ChatGPT-4.0,3/14/2023,"Twenty-five cases were selected, and the AI ??correctly diagnosed 21, resulting in an accuracy of 84%. Performed best on clinical and anatomopathological cases; struggled with microbiology-based cases; MCQ format with 4 options; retrospective evaluation","25 dermatology clinical cases (MCQ format) ""What is your diagnosis?"" cases from Anais Brasileiros de Dermatologia (2019–2023)","Pacheco, M.A., Martini, A.P.S. (2025). https://doi.org/10.1016/j.abd.2025.501143",Using ChatGPT 4.0 for diagnosis in Dermatology: performance analysis in clinical cases from Anais Brasileiros de Dermatologia,Diagnostic Performance,LLM Evaluation in Healthcare,2025,OpenAI GPT series,80
2548,English,Dermatological diagnosis using clinical + image + lab data: cases resolved by anatomopathological diagnosis ,Clinical Dermatology,82.00%,25,ChatGPT 4,ChatGPT-4.0,3/14/2023,"Twenty-five cases were selected, and the AI ??correctly diagnosed 21, resulting in an accuracy of 84%. Performed best on clinical and anatomopathological cases; struggled with microbiology-based cases; MCQ format with 4 options; retrospective evaluation","25 dermatology clinical cases (MCQ format) ""What is your diagnosis?"" cases from Anais Brasileiros de Dermatologia (2019–2023)","Pacheco, M.A., Martini, A.P.S. (2025). https://doi.org/10.1016/j.abd.2025.501143",Using ChatGPT 4.0 for diagnosis in Dermatology: performance analysis in clinical cases from Anais Brasileiros de Dermatologia,Diagnostic Performance,LLM Evaluation in Healthcare,2025,OpenAI GPT series,80
2549,English,Dermatological diagnosis using clinical + image + lab data: cases resolved using microbiological method.,Clinical Dermatology,60.00%,25,ChatGPT 4,ChatGPT-4.0,3/14/2023,"Twenty-five cases were selected, and the AI ??correctly diagnosed 21, resulting in an accuracy of 84%. Performed best on clinical and anatomopathological cases; struggled with microbiology-based cases; MCQ format with 4 options; retrospective evaluation","25 dermatology clinical cases (MCQ format) ""What is your diagnosis?"" cases from Anais Brasileiros de Dermatologia (2019–2023)","Pacheco, M.A., Martini, A.P.S. (2025). https://doi.org/10.1016/j.abd.2025.501143",Using ChatGPT 4.0 for diagnosis in Dermatology: performance analysis in clinical cases from Anais Brasileiros de Dermatologia,Diagnostic Performance,LLM Evaluation in Healthcare,2025,OpenAI GPT series,80
2550,English,Human scoring of dermatology treatment plans,"Complex clinical scenarios: psoriasis, acne, atopic dermatitis, bullous pemphigoid, melasma",73.83%,50,ChatGPT 4o,ChatGPT-4o,5/13/2024,Human evaluators ranked human-authored plans higher. GPT-4o ranked 6th; o3 ranked 11th. Statistically significant difference (p=0.0313). ICC=0.561,550 human evaluations of 60 plans (5 cases x 12 participants) from 5 synthetic clinical case vignettes authored by dermatologists,"Dipayan Sengupta, Saumya Panda, Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology   arXiv:2507.05716 [cs.AI]  July 8, 2025 https://doi.org/10.48550/arXiv.2507.05716",Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology,Evaluation of AI-generated treatment plans,Clinical Practice,2025,OpenAI GPT series,82
2551,English,Human scoring of dermatology treatment plans,"Complex clinical scenarios: psoriasis, acne, atopic dermatitis, bullous pemphigoid, melasma",69.74%,50,ChatGPT o3,ChatGPT o3,2/27/2025,Human evaluators ranked human-authored plans higher. GPT-4o ranked 6th; o3 ranked 11th. Statistically significant difference (p=0.0313). ICC=0.561,550 human evaluations of 60 plans (5 cases x 12 participants) from 5 synthetic clinical case vignettes authored by dermatologists,"Dipayan Sengupta, Saumya Panda, Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology   arXiv:2507.05716 [cs.AI]  July 8, 2025 https://doi.org/10.48550/arXiv.2507.05716",Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology,Evaluation of AI-generated treatment plans,Clinical Practice,2025,OpenAI GPT series,82
2552,English,AI (Gemini 2.5 Pro) scoring of treatment plans,"Complex clinical scenarios: psoriasis, acne, atopic dermatitis, bullous pemphigoid, melasma",82.00%,5,ChatGPT o3,ChatGPT o3,2/27/2025,"AI judge ranked o3 1st and GPT-4o 2nd, reversing human rankings. Human-authored plans scored lower on average (mean 6.79 vs 7.75 for AI). Significant evaluator effect observed (p=0.0313).",60 AI evaluations of 60 plans from 5 synthetic clinical case vignettes authored by dermatologists,"Dipayan Sengupta, Saumya Panda, Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology   arXiv:2507.05716 [cs.AI]  July 8, 2025 https://doi.org/10.48550/arXiv.2507.05716",Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology,Evaluation of AI-generated treatment plans,Clinical Practice,2025,OpenAI GPT series,82
2553,English,AI (Gemini 2.5 Pro) scoring of treatment plans,"Complex clinical scenarios: psoriasis, acne, atopic dermatitis, bullous pemphigoid, melasma",73.00%,5,ChatGPT 4o,ChatGPT-4o,5/13/2024,"AI judge ranked o3 1st and GPT-4o 2nd, reversing human rankings. Human-authored plans scored lower on average (mean 6.79 vs 7.75 for AI). Significant evaluator effect observed (p=0.0313).",60 AI evaluations of 60 plans from 5 synthetic clinical case vignettes authored by dermatologists,"Dipayan Sengupta, Saumya Panda, Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology   arXiv:2507.05716 [cs.AI]  July 8, 2025 https://doi.org/10.48550/arXiv.2507.05716",Divergent Realities: A Comparative Analysis of Human Expert vs. Artificial Intelligence Based Generation and Evaluation of Treatment Plans in Dermatology,Evaluation of AI-generated treatment plans,Clinical Practice,2025,OpenAI GPT series,82
2554,English,Accuracy of Image-based diagnostics: melanoma detection from Dermoscopic Images,Melanoma,53.10%,250,ChatGPT 4o,ChatGPT-4o,5/13/2024,Low specificity but high sensitivity,ISIC 2020,"Sattler S, Chetla N, Chen M, Chang J, Hage T, Korzenko A   Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study. JMIR Preprints. 09/07/2025:80391, DOI: 10.2196/preprints.80391,  URL: https://preprints.jmir.org/preprint/80391",Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,83
2555,English,F1 Score: Image-based diagnostics: melanoma detection from Dermoscopic Images,Melanoma,67.30%,250,ChatGPT 4o,ChatGPT-4o,5/13/2024,Balanced F1 score,ISIC 2020,"Sattler S, Chetla N, Chen M, Chang J, Hage T, Korzenko A   Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study. JMIR Preprints. 09/07/2025:80391, DOI: 10.2196/preprints.80391,  URL: https://preprints.jmir.org/preprint/80391",Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,83
2556,English,Precision: Image-based diagnostics: melanoma detection from Dermoscopic Images,Melanoma,53.00%,250,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Moderate precision, indicates false positives",ISIC 2020,"Sattler S, Chetla N, Chen M, Chang J, Hage T, Korzenko A   Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study. JMIR Preprints. 09/07/2025:80391, DOI: 10.2196/preprints.80391,  URL: https://preprints.jmir.org/preprint/80391",Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,83
2557,English,Sensitivity: Image-based diagnostics: melanoma detection from Dermoscopic Images,Melanoma,92.00%,250,ChatGPT 4o,ChatGPT-4o,5/13/2024,"High sensitivity, few missed melanomas",ISIC 2020,"Sattler S, Chetla N, Chen M, Chang J, Hage T, Korzenko A   Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study. JMIR Preprints. 09/07/2025:80391, DOI: 10.2196/preprints.80391,  URL: https://preprints.jmir.org/preprint/80391",Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,83
2558,English,SpecificityImage-based diagnostics: melanoma detection from Dermoscopic Images,Melanoma,10.50%,250,ChatGPT 4o,ChatGPT-4o,5/13/2024,"Very low specificity, many false positives",ISIC 2020,"Sattler S, Chetla N, Chen M, Chang J, Hage T, Korzenko A   Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study. JMIR Preprints. 09/07/2025:80391, DOI: 10.2196/preprints.80391,  URL: https://preprints.jmir.org/preprint/80391",Melanoma Detection by Dermatologists and ChatGPT-4o: A Comparative Evaluation Study (Preprint),Medical Records and Diagnostic Processes,Clinical Practice,2025,OpenAI GPT series,83
2559,English,Accuracy in AI-generated dermatologic images,20 most common dermatologic conditions; visual generation and identification,22.00%,200,ChatGPT 4o,ChatGPT-4o (June–July 2024),6/1/2024,Substantial bias found in skin tone representation; accuracy remains below clinically acceptable thresholds for visual diagnosis,Generated image dataset (4000 images across 4 models); 200-image subset manually labeled by dermatology residents,"Joerg L, Kabakova M, Wang JY, Austin E, Cohen M, Kurtti A, Jagdeo J. AI-generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study. J Eur Acad Dermatol Venereol. 2025 Jul 16. doi: 10.1111/jdv.20849. Epub ahead of print. PMID: 40668069.",Diagnostic Decision Support,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,84
2560,English,Accuracy in AI-generated dermatologic images,20 most common dermatologic conditions; visual generation and identification,12.20%,200,Midjourney,Midjourney,6/1/2024,Substantial bias found in skin tone representation; accuracy remains below clinically acceptable thresholds for visual diagnosis,Generated image dataset (4000 images across 4 models); 200-image subset manually labeled by dermatology residents,"Joerg L, Kabakova M, Wang JY, Austin E, Cohen M, Kurtti A, Jagdeo J. AI-generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study. J Eur Acad Dermatol Venereol. 2025 Jul 16. doi: 10.1111/jdv.20849. Epub ahead of print. PMID: 40668069.",Diagnostic Decision Support,Diagnostic Decision Support,Clinical Practice,2025,NOT LLM. Similar to diffusion-based models,84
2561,English,Accuracy in AI-generated dermatologic images,20 most common dermatologic conditions; visual generation and identification,22.50%,200,Stable Diffusion,Stable Diffusion,6/1/2024,Substantial bias found in skin tone representation; accuracy remains below clinically acceptable thresholds for visual diagnosis,Generated image dataset (4000 images across 4 models); 200-image subset manually labeled by dermatology residents,"Joerg L, Kabakova M, Wang JY, Austin E, Cohen M, Kurtti A, Jagdeo J. AI-generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study. J Eur Acad Dermatol Venereol. 2025 Jul 16. doi: 10.1111/jdv.20849. Epub ahead of print. PMID: 40668069.",Diagnostic Decision Support,Diagnostic Decision Support,Clinical Practice,2025,NOT LLM. Uses CLIP (Contrastive Language–Image Pre-training) for text-image alignment,84
2562,English,Bias in AI-generated dermatologic images,20 most common dermatologic conditions; visual generation and identification,0.94%,200,Adobe Firefly,Adobe Firefly,6/1/2024,Substantial bias found in skin tone representation; accuracy remains below clinically acceptable thresholds for visual diagnosis,Generated image dataset (4000 images across 4 models); 200-image subset manually labeled by dermatology residents,"Joerg L, Kabakova M, Wang JY, Austin E, Cohen M, Kurtti A, Jagdeo J. AI-generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study. J Eur Acad Dermatol Venereol. 2025 Jul 16. doi: 10.1111/jdv.20849. Epub ahead of print. PMID: 40668069.",Diagnostic Decision Support,Diagnostic Decision Support,Clinical Practice,2025,"NOT LLM. text-to-image model, likely based on diffusion techniques",84
2563,English,Bias in AI-generated dermatologic images,20 most common dermatologic conditions; visual generation and identification,100.00%,200,Adobe Firefly,Adobe Firefly,6/1/2024,Substantial bias found in skin tone representation; accuracy remains below clinically acceptable thresholds for visual diagnosis,Generated image dataset (4000 images across 4 models); 200-image subset manually labeled by dermatology residents,"Joerg L, Kabakova M, Wang JY, Austin E, Cohen M, Kurtti A, Jagdeo J. AI-generated dermatologic images show deficient skin tone diversity and poor diagnostic accuracy: An experimental study. J Eur Acad Dermatol Venereol. 2025 Jul 16. doi: 10.1111/jdv.20849. Epub ahead of print. PMID: 40668069.",Diagnostic Decision Support,Diagnostic Decision Support,Clinical Practice,2025,"NOT LLM. text-to-image model, likely based on diffusion techniques",84
2564,English,Accuracy of Clinic letter generation in dermatology,Dermatology clinical communication and documentation,92.60%,12,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT achieved the highest accuracy and satisfaction scores but generated complex, less readable letters; compared against Heidi and human transcription. Study used scripted interactions in a simulated setting and senior dermatologists for evaluation.","Scripted clinical cases (4) enacted by clinicians; not publicly available, small-scale simulated dataset. 12  letters (4 cases × 3 methods: ChatGPT, Heidi, traditional)","Farooq F, Cooper H, Shipman A, Michell CD. Artificial intelligence verses traditional method in generating dermatology consultation letters: a pilot study comparing accuracy, readability, and efficiency. Clin Exp Dermatol. 2025 Jul 17:llaf323. doi: 10.1093/ced/llaf323. Epub ahead of print. PMID: 40674470.",Clinical Documentation,Clinical Documentation,Clinical Practice,2025,OpenAI GPT series,85
2565,English,Satisfaction: Clinic letter generation in dermatology,Dermatology clinical communication and documentation,79.50%,12,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT achieved the highest accuracy and satisfaction scores but generated complex, less readable letters; compared against Heidi and human transcription. Study used scripted interactions in a simulated setting and senior dermatologists for evaluation.","Scripted clinical cases (4) enacted by clinicians; not publicly available, small-scale simulated dataset. 12  letters (4 cases × 3 methods: ChatGPT, Heidi, traditional)","Farooq F, Cooper H, Shipman A, Michell CD. Artificial intelligence verses traditional method in generating dermatology consultation letters: a pilot study comparing accuracy, readability, and efficiency. Clin Exp Dermatol. 2025 Jul 17:llaf323. doi: 10.1093/ced/llaf323. Epub ahead of print. PMID: 40674470.",Clinical Documentation,Clinical Documentation,Clinical Practice,2025,OpenAI GPT series,85
2566,English,Readability: Clinic letter generation in dermatology,Dermatology clinical communication and documentation,30.00%,12,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT achieved the highest accuracy and satisfaction scores but generated complex, less readable letters; compared against Heidi and human transcription. Study used scripted interactions in a simulated setting and senior dermatologists for evaluation.","Scripted clinical cases (4) enacted by clinicians; not publicly available, small-scale simulated dataset. 12  letters (4 cases × 3 methods: ChatGPT, Heidi, traditional)","Farooq F, Cooper H, Shipman A, Michell CD. Artificial intelligence verses traditional method in generating dermatology consultation letters: a pilot study comparing accuracy, readability, and efficiency. Clin Exp Dermatol. 2025 Jul 17:llaf323. doi: 10.1093/ced/llaf323. Epub ahead of print. PMID: 40674470.",Clinical Documentation,Clinical Documentation,Clinical Practice,2025,OpenAI GPT series,85
2567,English,Time Efficiency: Clinic letter generation in dermatology,Dermatology clinical communication and documentation,95.80%,12,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT achieved the highest accuracy and satisfaction scores but generated complex, less readable letters; compared against Heidi and human transcription. Study used scripted interactions in a simulated setting and senior dermatologists for evaluation.","Scripted clinical cases (4) enacted by clinicians; not publicly available, small-scale simulated dataset. 12  letters (4 cases × 3 methods: ChatGPT, Heidi, traditional)","Farooq F, Cooper H, Shipman A, Michell CD. Artificial intelligence verses traditional method in generating dermatology consultation letters: a pilot study comparing accuracy, readability, and efficiency. Clin Exp Dermatol. 2025 Jul 17:llaf323. doi: 10.1093/ced/llaf323. Epub ahead of print. PMID: 40674470.",Clinical Documentation,Clinical Documentation,Clinical Practice,2025,OpenAI GPT series,85
2568,English,Accuracy of answers to exam questions,General Dermatology,79.46%,1762,ChatGPT 4,ChatGPT-4.0,3/14/2023,"ChatGPT-4.0 showed the highest overall accuracy of 79.46% (95% CI: 75.36-83.30%, I2 = 72.79%, P value = 0.00) compared to ChatGPT-3.5, which showed an overall accuracy of 61.07% (95% CI: 57.36-64.72%, I2 = 72.97%, P value = 0.00). The passing percentage for specialty dermatology certification examinations varies by jurisdiction, but several studies in our study suggest that it lies typically around the 60% mark.[11,20] Based on its performance, both GPT-3.5 and GPT-4.0 versions of ChatGPT generally meet this standard. ","Text-based, single-best-answer multiple-choice questions drawn from national specialty exams, institutional question banks, and dermatologist-authored test sets. 13 publications. 5 countries, 4 languages. The meta-analysis aggregated performance results from over a dozen studies evaluating ChatGPT on hundreds of dermatology board-style certification questions. ",Andrew A. A. Meta-Analysis of ChatGPT's Performance on Dermatology Specialty-Level (Board-Style) Certification QuestionsAbstract. Indian Dermatol Online J. 2025 Jul 23. doi: 10.4103/idoj.idoj_1250_24. Epub ahead of print. PMID: 40709867.,A Meta-Analysis of ChatGPT's Performance on Dermatology Specialty-Level (Board-Style) Certification Questions,Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,86
2569,English,Accuracy of answers to exam questions,General Dermatology,61.07%,3429,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT-4.0 showed the highest overall accuracy of 79.46% (95% CI: 75.36-83.30%, I2 = 72.79%, P value = 0.00) compared to ChatGPT-3.5, which showed an overall accuracy of 61.07% (95% CI: 57.36-64.72%, I2 = 72.97%, P value = 0.00). The passing percentage for specialty dermatology certification examinations varies by jurisdiction, but several studies in our study suggest that it lies typically around the 60% mark.[11,20] Based on its performance, both GPT-3.5 and GPT-4.0 versions of ChatGPT generally meet this standard. ","Text-based, single-best-answer multiple-choice questions drawn from national specialty exams, institutional question banks, and dermatologist-authored test sets. 13 publications. 5 countries, 4 languages. The meta-analysis aggregated performance results from over a dozen studies evaluating ChatGPT on hundreds of dermatology board-style certification questions. ",Andrew A. A. Meta-Analysis of ChatGPT's Performance on Dermatology Specialty-Level (Board-Style) Certification QuestionsAbstract. Indian Dermatol Online J. 2025 Jul 23. doi: 10.4103/idoj.idoj_1250_24. Epub ahead of print. PMID: 40709867.,A Meta-Analysis of ChatGPT's Performance on Dermatology Specialty-Level (Board-Style) Certification Questions,Examinations and Practice Questions,Professional Education,2025,OpenAI GPT series,86
2570,English,Dermatological diagnosis using standardized physical examination findings,"Clinical Pediatric Dermatology, 178 pediatric dermatologic conditions",59.30%,178,ChatGPT 3.5,ChatGPT-3.5,11/30/2022,"ChatGPT 3.5 was used to acquire answers to the prompt “diagnosis of [physical exam description]” using standardized physical examination findings from 178 pediatric dermatologic conditions in the Hurwitz Clinical Pediatric Dermatology textbook. Despite the availability of more sophisticated models of ChatGPT that can utilize photo input, ChatGPT 3.5 was specifically used as it is free and the most accessible to parents of patients. Based on inputted physical exam findings, ChatGPT successfully identified 59.30% of pathologies. ",Standardized physical examination findings from 178 pediatric dermatologic conditions in the Hurwitz Clinical Pediatric Dermatology textbook. ,"Perz C., Presley C.L., Hurley M., Swink S. 0336 A survey and analysis ChatGPT's generated differential diagnoses utilizing physical exam descriptions from a pediatric dermatology textbook Journal of Investigative Dermatology, Volume 145, Issue 8, S58",0336 A survey and analysis ChatGPT's generated differential diagnoses utilizing physical exam descriptions from a pediatric dermatology textbook,Diagnostic Decision Support,Clinical Practice,2025,OpenAI GPT series,87
2571,English,Image-based diagnostics,Shingles,100.00%,2,ChatGPT 5,ChatGPT-5,8/7/2025,"42 yr old female, less typical appearance: ""I’m not a doctor, but the rash in your photo appears as a small cluster of red, slightly raised spots on the lower back/side. This kind of presentation could have several possible causes, including: Contact dermatitis – skin reaction from something rubbing against or irritating the area (e.g., clothing, detergent, belt).; Allergic reaction – to skin products, laundry soap, or environmental allergens; Insect bites – sometimes grouped together if multiple bites occur in a short area; Heat rash – blocked sweat glands causing small red bumps, especially if skin gets warm or sweaty under clothing; Shingles (Herpes zoster) – often starts as a patch of red bumps or blisters on one side of the body, sometimes with tingling or burning before pain. ? You should seek medical advice promptly if: The rash is painful, blistering, or spreading quickly; You have fever, chills, or feel generally unwell. It appears on one side in a band-like pattern with burning pain (possible shingles — early treatment is important).; It’s persistent or worsening despite avoiding irritants. If you’d like, I can help you narrow the possibilities by asking a few quick questions about symptoms, onset, and exposure history.  Second image, 61 year old male, more typical case: ""I’m not a doctor, but the rash in this photo shows red patches with small grouped bumps or vesicles along the side of the neck, with at least one larger, crusted lesion above. This appearance could fit with: Shingles (herpes zoster) – if it’s on one side only, in a strip or patch, possibly with tingling, burning, or pain. Irritant or allergic contact dermatitis – from shaving products, fragrances, or shirt collar friction. Herpes simplex virus (HSV) – can appear on the neck or face, sometimes mistaken for shingles. Less commonly, impetigo or secondary bacterial infection – especially if there’s yellow crusting. Things to check: Is it only on one side? (Shingles usually is.) Was there pain/tingling before the rash? (Common in shingles.) Any new soaps, laundry detergents, aftershave, or perfumes? (Points to contact dermatitis.) Is there fever or general malaise? (Could point to viral cause.) Recommendation: Because this is on the neck and involves vesicles: See a healthcare provider promptly — early diagnosis matters if it’s shingles or HSV. If shingles or HSV is suspected, antivirals work best if started within 72 hours of onset. Avoid scratching, keep the area clean, and avoid close skin contact with vulnerable individuals until diagnosis is clear.""",Private database of patient stories and images,"Gabashvili, Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis, 2025",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Patient Education,2025,OpenAI GPT series,88
2572,English,Image-based diagnostics (dark skin) and treatment recommendations,Acne,100.00%,1,ChatGPT 5,ChatGPT-5,8/7/2025,"""I’m not a doctor, but from the photo, the skin appears to have small, slightly raised bumps and visible pores with a bit of uneven texture. This is consistent with mild acne (closed comedones and a few small inflammatory spots) or post-inflammatory changes from previous acne. Possible causes
Excess sebum production clogging pores. Hormonal changes affecting oil glands. Cosmetic products that are not non-comedogenic. Friction or occlusion (phones, pillowcases, masks). Dietary or lifestyle factors in some individuals. General treatment approach:  At-home care (over-the-counter): Gentle cleansing twice daily with a mild, non-foaming cleanser. Topical actives: Salicylic acid (BHA) 0.5–2% for unclogging pores. Benzoyl peroxide 2.5–5% for reducing acne bacteria. Topical retinoid (adapalene 0.1%) for promoting cell turnover. Oil-free, non-comedogenic moisturiser to maintain skin barrier. Daily sunscreen (SPF 30+) — helps prevent dark marks from healing pimples. Professional options: A dermatologist can prescribe topical or oral retinoids, antibiotics, or hormonal treatments (if relevant). Chemical peels or gentle resurfacing may improve texture and pore appearance. Things to avoid Harsh scrubbing (can worsen inflammation). Picking or squeezing (increases risk of scarring). Overwashing or overusing actives (can damage skin barrier).""",Private database of patient stories and images,"Gabashvili, Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis, 2025",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Patient Education,2025,OpenAI GPT series,88
2573,English,Image-based diagnostics (dark skin) and treatment recommendations,Dermatosis Papulosa Nigra (DPN),100.00%,1,ChatGPT 5,ChatGPT-5,8/7/2025,"""I’m not able to give a medical diagnosis, but the image you provided shows multiple small, dark-brown to black, slightly raised spots on the skin — especially clustered around the eye and upper cheek. They appear consistent with what’s known as dermatosis papulosa nigra (DPN), which is a common, benign condition seen more often in people with darker skin tones. Key features of DPN: Small, smooth, or slightly rough papules (bumps); Usually brown to black in color; Typically appear on the face, neck, and upper trunk; More common with age and in certain ethnic backgrounds; Harmless, but some people remove them for cosmetic reasons. If the bumps change rapidly, bleed, ulcerate, or cause discomfort, it’s best to get them evaluated by a dermatologist to rule out other conditions.""",Private database of patient stories and images,"Gabashvili, Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis, 2025",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Patient Education,2025,OpenAI GPT series,88
2574,English,Diagnostics and Treatment recommendations,PATM,100.00%,3,ChatGPT 5,ChatGPT-5,8/7/2025,"""PATM (“People Allergic to Me”) is not a recognized medical diagnosis in mainstream medicine — it’s more of a self-described phenomenon where someone believes people nearby react with coughing, sneezing, or discomfort because of them. Since the doctor has already run extensive physical tests and nothing serious has been found, the next steps should focus on:..""",Private database of patient stories and images,"Gabashvili, Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis, 2025",Evaluating LLMs in Patient-Facing Medicine: A Dermatology-Centered Systematic Review and Meta-Analysis,Medical Records and Diagnostic Processes,Patient Education,2025,OpenAI GPT series,88