Purpose of review Artificial intelligence (AI) and machine learning (ML) are rapidly transforming healthcare, but their adoption in nephrology and dialysis remains relatively limited. Recent findings This review highlights key applications of AI in kidney disease, including prognostic modeling, imaging, personalized anemia and fluid management, patient engagement, and research acceleration. While numerous studies demonstrate improved prediction accuracy and clinical insights, translation into routine practice is rare. Examples such as the Anemia Control Model (ACM) demonstrate that AI can simultaneously improve clinical outcomes and reduce costs, though widespread adoption will require rigorous validation, seamless integration into clinical workflows, regulatory approval, and above all, clinician trust. Summary AI in nephrology shows promise for personalized care and cost reduction, as demonstrated by tools like the Anemia Control Model. Yet, broad adoption requires rigorous validation, seamless workflow integration, regulatory clearance, and clinician trust. Future opportunities include digital twins, large language models, and multiomics integration, with AI poised to enhance both patient outcomes and system performance.
Neri et al. (2025) studied this question.