Artificial intelligence (AI) is rapidly transforming the field of nephrology by enhancing the diagnosis, prognosis, and management of kidney diseases through advanced data-driven techniques. AI-driven medical image analysis using deep learning and radiomics enables early detection of structural abnormalities in chronic kidney disease (CKD) and diabetic kidney disease (DKD), improving diagnostic accuracy and enabling standardized interpretation of ultrasound, computed tomography (CT), and magnetic resonance imaging (MRI). Machine learning (ML)models that integrate clinical, laboratory, and genetic data surpass traditional risk equations in predicting CKD progression, dialysis initiation, and mortality, facilitating personalized monitoring and intervention. In renal pathology, AI-powered digital pathology tools automate lesion detection, quantification, and classification from biopsy specimens, offering objective metrics that correlate with clinical outcomes. Within kidney transplantation, AI refines donor–recipient matching, predicts acute rejection and graft survival, and augments biopsy interpretation to optimize use of scarce donor organs. AI-enabled decision support systems and real-time monitoring algorithms in dialysis and continuous kidney replacement therapy personalize treatment parameters, enhance symptom control, and predict complications. Despite these advances, challenges related to data bias, model interpretability, ethical considerations, and clinical integration must be addressed. Prospects include the integration of multi-omics data, federated learning to protect patient privacy, explainable AI models for transparent decision-making, and telemedicine applications to extend nephrology expertise globally. Continued interdisciplinary collaboration and rigorous clinical validation will be essential to fully harness AI’s potential and usher in an era of precision nephrology.
Mosleh et al. (Tue,) studied this question.