Artificial Intelligence–Enabled Diagnostics and Biomarker Discovery in Nephropathies

AbstractNephropathies comprise a broad range of kidney disorders, including chronic kidney disease(CKD), acute kidney injury (AKI), diabetic nephropathy, glomerular diseases, and inherited renalconditions, all of which contribute significantly to global morbidity, mortality, and economicburden. Early diagnosis and accurate prediction of disease progression remain major clinicalchallenges because conventional diagnostic approaches such as serum creatinine, estimatedglomerular filtration rate (eGFR), albuminuria, and renal biopsy often lack early sensitivity,specificity, or practicality for routine large-scale screening. Many renal disorders progresssilently until substantial nephron damage has occurred, limiting the effectiveness of therapeuticintervention. In recent years, artificial intelligence (AI) has emerged as a powerful tool capableof transforming nephrology by enhancing diagnostic precision, enabling early risk prediction,and accelerating biomarker discovery. AI technologies—including machine learning, deeplearning, neural networks, natural language processing, and data-driven predictive analytics—can process complex, high-dimensional datasets from electronic health records, imaging systems,histopathology, wearable sensors, and multi-omics platforms. These capabilities allow AI touncover clinically meaningful patterns that may not be detectable through traditional statistical ordiagnostic methods. Across nephrology, AI applications have shown promise in early CKD andAKI detection, automated interpretation of renal imaging and biopsy specimens, progressionforecasting, and personalized treatment decision support. Simultaneously, AI-based integration ofgenomics, proteomics, transcriptomics, and metabolomics has facilitated the identification ofemerging biomarkers such as NGAL, KIM-1, cystatin C, and molecular biomarker panels withimproved diagnostic and prognostic potential. Current evidence suggests that AI maysignificantly improve early detection, reduce reliance on invasive procedures, strengthen clinicaldecision-making, and support precision medicine strategies in kidney disease management.However, barriers including inconsistent data quality, model bias, poor interpretability, ethicalconcerns, privacy risks, and inadequate prospective validation must be addressed beforewidespread clinical adoption can occur. Future advancements in explainable AI, federatedlearning, digital biomarkers, and wearable-integrated nephrology may further expand AI’s role inkidney care. This review explores the evolving contributions of AI to nephropathy diagnosticsand biomarker discovery, emphasizing both current innovations and future translationalopportunities in precision nephrology.