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Browse normalized, publication-ready research with direct links to its evidence and source.
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Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
Loading articles data…
Readable research linked to original sources
Browse normalized, publication-ready research with direct links to its evidence and source.
106 articles
Newest firstBackground The increasing prevalence of cannabis use has motivated researchers to develop computational behavioral models that predict usage patterns and related health impacts in naturalistic environments. However, the opaque nature of many artificial intelligence (AI) systems limits users' ability to interpret outputs and undermines trust. Existing explainable artificial intelligence techniques often remain over…
Open article record in new tab ↗Background Social media influencers who promote e-cigarettes on Instagram or TikTok for tobacco brands use marketing tactics to increase the appeal of their promotional content, for example, depicting e-cigarettes alongside healthy lifestyle or entertainment imagery that could decrease youths' risk perceptions of e-cigarettes. Monitoring the prevalence of such content on social media using computer vision and gene…
Background Machine learning (ML) has growing potential to support early identification of high-risk pregnancies in resource-constrained settings. However, most studies focus on model development and predictive performance, with less attention to the health-system processes required to generate ML-ready data and translate risk information into clinical action. The Mlinde Mama Project in Tanzania combined Group Ante…
Background: Digital health technologies, spanning mobile applications, telemedicine, and AI-driven platforms, are rapidly reshaping healthcare delivery globally. Although Generation Z university students are classified as digital natives, empirical data evaluating their eHealth literacy, technology acceptance, and specific trust barriers in developing South Asian nations like Sri Lanka remain scarce. Objective: Th…
Open article record in new tab ↗BackgroundLarge language models (LLMs) are increasingly deployed in healthcare, where they may adopt different stakeholder perspectives, yet the effect of role-prompting on clinical ethical reasoning remains poorly characterized. MethodsWe evaluated three frontier LLMs: Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro across 25 ethically complex medical cases. Each model responded from three stakeholder perspectives (…
BackgroundStandardized evaluation of agentic artificial intelligence (AI) for medication management is lacking. Given the potential lethality of medication errors endorsed or missed by AI, performance evaluation constructs are essential. The purpose of this evaluation was to develop a standardized grading framework for performance evaluation of medication management tasks. MethodsA mixed-methods approach was under…
Open article record in new tab ↗Large language models (LLMs) such as ChatGPT are rapidly reshaping healthcare education and simulation-based training in non-technical skills (NTS), yet no bibliometric analysis has mapped this landscape. We searched seven open-access databases (OpenAlex, PubMed, Europe PMC, Crossref, Semantic Scholar, CORE, DOAJ) for English-language publications from January 2020 to March 2026. From 100,277 initial records, a se…
BackgroundElectrolyte replacement is ubiquitous in the acute care setting, but its familiarity cannot belie that even small dosing errors with potassium can cause lethal cardiac arrhythmias. Recently, MedAgentBench offered a benchmark for agentic artificial intelligence (AI) including the ability to correctly dose potassium based on a single rule; however, this does not adequately reflect the clinical complexity o…
Open article record in new tab ↗BackgroundArtificial Intelligence (AI) is increasingly integrated into healthcare systems worldwide and medical schools worldwide have begun integrating AI into their curricula. The healthcare system in Iraq is currently undergoing development and AI has not yet been adopted in clinical practice in Erbil; in addition, no formal AI instruction has been incorporated into the medical education curriculum. The aim of…
Open article record in new tab ↗The integration of artificial intelligence (AI) in cardiology requires healthcare worker acceptance for successful implementation. Understanding attitudes and educational needs is crucial for developing effective training programs. A cross-sectional survey was conducted among 408 healthcare workers treating cardiac diseases in Riyadh, Saudi Arabia. We assessed AI acceptance, knowledge levels, and training preferen…
Medication adherence among patients with diabetes remains suboptimal in low- and middle-income countries, including Nigeria. Emerging digital health interventions such as AI-powered virtual support may be associated with improved adherence behaviours. This study examined self-care competence and perceived AI-powered virtual support as predictors of medication adherence among patients with diabetes. A cross-section…
Open article record in new tab ↗BackgroundLarge Language Model (LLM) chatbots are increasingly used for exercise and fitness topics, yet users experience with these tools remains understudied. MethodsThis study is a national survey of U.S. adults who have used an LLM chatbot for exercise-related topics in the past month. Participants answered questions about the exercise-related topics for which they used LLM chatbots, their perceptions of these…
Open article record in new tab ↗IntroductionLarge language models are increasingly being used in healthcare. In interventional pain medicine, clinical reasoning is essential for procedural planning. Prior studies show that simplified prompts reduce clinical detail in AI-generated responses. It remains unclear whether this reflects knowledge loss or simply prompt-driven suppression of information. MethodsWe performed a controlled comparative stud…
Open article record in new tab ↗IntroductionTimely, protocol-adherent clinical decisions are crucial for reducing neonatal mortality in low-resource settings. Translating extensive national guidelines into bedside practice remains challenging. ObjectiveWe developed and evaluated AIFYA, a human-supervised, large language model (LLM)-based clinical decision support system (CDSS) aligned with Kenyas national newborn care protocols. MethodsThis pros…
AimsWe aimed to examine public perceptions of sharing various types of health data relevant for AI development, including electronic health records, audio recordings of consultations, medical images, and genetic information, with actors from either the public or the private sectors. MethodsWe analysed data from 38,740 participants of the Health in Central Denmark survey conducted in 2024. Participants were asked w…
Open article record in new tab ↗BackgroundSnakebite envenoming is a significant global health crisis that has been long neglected as a global health priority. It is a huge problem for rural communities of low and middle-income countries, India accounts for the largest proportion of snakebite deaths globally. Timely identification of venomous snakebite and its syndromic pattern is essential for effective administration of antivenom and supportive…
PurposeTo evaluate whether large language models (LLMs) can enhance clinician-patient communication by simplifying radiology reports to improve patient readability and comprehension. MethodsA randomised controlled trial was conducted at a single healthcare service for patients undergoing X-ray, ultrasound or computed tomography between May 2025 and June 2025. Participants were randomised in a 1:1 ratio to receive…
BackgroundIn the world of Artificial Intelligence (AI), Generative Pretrained Transformer-3 (GPT-3), has gained significant popularity for its demonstrated potential in medical education and diagnostics. RationaleWhile AI has shown promising results in healthcare thus far, its understanding of ocular urgencies, particularly uveitis, demands a focused investigation. MethodsThis study explored the application of Cha…
Open article record in new tab ↗Withdrawal StatementThis article has been withdrawn by medRxiv because it was submitted with false information.
Open article record in new tab ↗ImportanceEmerging evidence suggests healthcare AI systems may exhibit deceptive alignment (appearing safe during validation while optimizing for misaligned objectives in deployment) and evaluation awareness (detecting and adapting behavior during audits), undermining regulatory validation frameworks. ObjectiveTo quantify the performance of multi-layer red-teaming approaches in detecting sophisticated healthcare A…
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