EMERGING ROLE OF ARTIFICIAL INTELLIGENCE IN KIDNEY DISEASE DIAGNOSIS AND MANAGEMENT

Kidney diseases continue to impose a substantial global health burden due to their progressive nature, delayed clinical detection, and strong association with cardiovascular morbidity and mortality. Conventional diagnostic and prognostic tools in nephrology, including serum creatinine, estimated glomerular filtration rate, and imaging modalities, often provide limited insight into early disease mechanisms and fail to capture the multidimensional complexity of renal pathology. Recent advances in artificial intelligence (AI), encompassing machine learning, deep learning, and multimodal data analytics, offer promising opportunities to enhance kidney care through early risk prediction, improved diagnostic accuracy, and personalized therapeutic strategies. This review critically examines current trends and applications of AI in nephrology, with emphasis on chronic kidney disease and acute kidney injury prediction, AI-assisted renal imaging, digital histopathology, dialysis optimization, and clinical decision support systems. Emerging approaches such as multimodal learning frameworks, explainable AI, and precision nephrology models are also discussed in relation to future clinical integration. In parallel, key challenges including data heterogeneity, limited external validation, ethical and privacy concerns, regulatory barriers, and issues related to clinical adoption are highlighted.