Machine learning (ML) models have been deployed extensively to predict the progression of CKD by analyzing longitudinal patient records and diverse biomarkers [2], [5]. Advanced ML techniques, such as support vector machines and artificial neural networks, have been utilized to handle complex non-linear interactions in clinical datasets [4], [5]. These computational algorithms exhibit strong predictive performance by extracting detailed patterns from multi-dimensional data sources [2], [5]. Notably, early identification of CKD via ML techniques contributes to timely treatment interventions, thereby potentially slowing disease progression and improving life expectancy [2], [5]. This approach allows for the stratification of patients according to disease risk, setting a foundation for precision medicine in nephrology [2], [4].

Deep learning (DL) methods, a subset of AI, offer considerable promise in processing high-dimensional imaging data that are crucial for CKD diagnosis [1], [6]. The application of convolutional neural networks to renal imaging has proven effective in identifying subtle texture variations associated with early kidney damage [6], [1]. These DL-based approaches are not only capable of accurately detecting early pathological changes but also contribute to robust prognostic evaluations [1], [6]. Moreover, the integration of DL models in clinical workflows enhances the sensitivity and specificity of kidney disease detection, ensuring that critical information is not overlooked [1], [6]. Such techniques thereby support the development of automated, clinician-assisting systems for CKD management [6], [1].

The use of ensemble machine learning techniques in predicting CKD outcomes has shown substantial improvements over individual classifiers [7], [8]. Ensemble methods combine the strengths of various algorithms, thereby reducing variance and improving overall prediction accuracy [7], [8]. By stacking or merging multiple ML models, researchers have achieved higher performance in identifying high-risk patients and forecasting disease progression [7], [8]. This methodological advancement supports a more holistic interpretation of the multifactorial and heterogeneous nature of CKD [7], [8]. Consequently, ensemble approaches are increasingly recognized for their robustness and reliability in clinical prognosis applications [7], [8].

The development of feature selection techniques has been critical in enhancing the predictive accuracy of AI models for CKD detection [9], [5]. These methods facilitate the identification of the most salient clinical and laboratory features that signal early kidney dysfunction [9], [5]. By employing algorithms such as forward selection and genetic algorithms, researchers can filter out redundant data and focus on meaningful indicators [9], [5]. This refinement of data inputs leads to model simplification without sacrificing performance, ultimately making AI systems more interpretable and clinically relevant [9], [5]. Such techniques are essential for building trust in AI-driven diagnostic tools among healthcare professionals [5], [9].

Several studies have underscored the importance of early diagnosis in CKD, highlighting that many patients remain asymptomatic until significant renal damage occurs [3], [10]. AI algorithms offer a proactive approach by detecting subclinical manifestations before conventional markers like serum creatinine levels become abnormal [3], [10]. This early detection capability enables the initiation of preventive measures and personalized interventions, which are critical to reducing the overall burden of CKD [3], [10]. The utilization of AI methods thus represents a paradigm shift in nephrology, shifting focus from reactive to proactive healthcare management [3], [10]. In addition, the early identification of high-risk patients significantly contributes to cost savings and more efficient healthcare resource allocation [3], [10].

Recent advances in ML have emphasized the promise of integrating support vector machine (SVM) and artificial neural network (ANN) algorithms in CKD detection [4], [11]. Research has shown that these ML models can achieve high accuracy in diagnosing CKD from varied clinical datasets [4], [11]. Such systems are designed to learn from high-dimensional clinical information, enabling precise classification between healthy renal function and early-stage CKD [4], [11]. Furthermore, these algorithms offer opportunities to explore novel biomarkers and risk factors that were previously under-recognized in traditional models [4], [11]. Consequently, the continued optimization of SVM and ANN techniques is anticipated to further enhance early disease recognition [4], [11].

Interpretability remains a critical factor in the clinical adoption of AI-based diagnostics, as it is essential for clinicians to understand the rationale behind automated predictions [12], [13]. Efforts to develop explainable AI frameworks have helped demystify the “black-box” nature of some deep learning models used in CKD detection [12], [13]. By elucidating how individual features contribute to risk assessments, these models foster transparency and facilitate informed clinical decision-making [12], [13]. The enhanced interpretability also promotes clinician trust in AI-generated recommendations, which is paramount for successful clinical integration [12], [13]. As a result, a dual focus on predictive accuracy and model explainability is driving significant progress in the field [12], [13].

Data quality and quantity underpin the success of any AI-driven diagnostic model, and this is particularly true in the context of CKD where heterogeneous patient data are common [14], [15]. High-quality datasets enable AI models to extract reliable patterns and generalize effectively across diverse populations [14], [15]. Researchers have highlighted the importance of extensive data preprocessing to remove noise and address missing values, thereby ensuring robust model performance [14], [15]. Additionally, advanced data mining techniques have been applied to identify and reduce data imbalances, which are prevalent in CKD datasets [14], [15]. Such preprocessing efforts are critical in developing models that not only perform well in controlled studies but also translate effectively into real-world clinical settings [14], [15].

The integration of clinical decision support systems (CDSS) with AI algorithms has been proposed to further enhance CKD management by assisting clinicians in diagnostics and treatment planning [16], [17]. These systems leverage objective predictions from AI models to aid physicians in determining optimal care strategies for patients at various stages of CKD [16], [17]. By combining clinical expertise with AI-powered insights, CDSS can offer more nuanced and timely interventions, ultimately improving patient outcomes [16], [17]. The objective nature of AI predictions helps in reducing inter-observer variability and standardizes the assessment process across different healthcare settings [16], [17]. This integration exemplifies how technology can synergistically work with traditional clinical wisdom to address complex diseases like CKD [16], [17].