Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions.

Artificial intelligence (AI) and machine learning (ML) applications have emerged as transformative technologies in nephrology, particularly in dialysis care. The availability of multimodal datasets including electronic health records, hemodialysis machine data, laboratory values, and imaging has enabled the development of sophisticated prognostic, diagnostic, and treatment support tools. This comprehensive review narratively examines current AI/ML applications in dialysis, evaluates their clinical performance, and identifies future research directions. We conducted a comprehensive literature search of PubMed, Web of Science, and other major databases from 2020 to 2025, focusing on peer-reviewed studies that employed AI/ML techniques in dialysis care. Studies were categorised by application domain and analysed for methodology, performance metrics, and clinical implications. Our analysis identified five major application domains: (1) prediction and prognosis, including intradialytic hypotension (IDH) prediction with AUROC values ranging 0.89-0.95, mortality prediction achieving C-indices up to 0.83, and hospitalisation risk assessment; (2) early detection of chronic kidney disease (CKD) progression and dialysis risk; (3) clinical decision support for anaemia management and treatment optimisation; (4) vascular access monitoring with AI-driven image analysis achieving AUROC ≈ 0.96; and (5) natural language processing (NLP) applications for symptom detection. Federated learning (FL) approaches are emerging to enable multi-centre collaboration while preserving data privacy. AI/ML technologies demonstrate significant promise in enhancing dialysis care through improved prediction accuracy, personalised treatment approaches, and clinical decision support. However, widespread clinical adoption remains limited due to challenges including data privacy concerns, model interpretability issues, regulatory complexity, and the need for diverse, representative datasets.