AI-Assisted Detection Of Early Renal Dysfunction: Advancing Precision Medicine In Kidney Care

Abstract

Early renal dysfunction frequently progresses without overt clinical manifestations, limiting opportunities for timely nephroprotective intervention. Conventional diagnostic indicators, including serum creatinine and estimated glomerular filtration rate, often reflect renal impairment only after substantial functional loss has occurred. This study evaluated the role of an artificial intelligence–assisted analytical framework for the early identification of renal dysfunction using routinely available clinical and laboratory data. A retrospective analytical design was applied to electronic medical records of adult patients undergoing routine renal evaluation at a tertiary nephrology center. Structured variables encompassing renal biomarkers, blood pressure measurements, and key comorbid conditions were analyzed through parallel conventional clinical assessment and AI-assisted risk stratification. The AI-based approach assessed nonlinear interactions among renal parameters to classify patients into renal risk categories, enabling comparison with standard clinical classification. The findings demonstrated substantial concordance between AI-assisted and clinician-based risk assessment, with the AI model exhibiting heightened sensitivity in identifying individuals with moderate and subclinical renal risk who may be overlooked by fixed threshold–based evaluation. Predictive patterns generated by the model aligned with established nephropathological mechanisms, supporting clinical interpretability. Overall, the study highlights the potential of AI-assisted diagnostics to enhance early renal risk detection, refine stratification accuracy, and support precision-oriented kidney care, while complementing rather than replacing clinical judgment.