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

Clinical Snapshot

Key Findings

• P Value: Not applicable for narrative review

• Effect Size: AUROC values 0.89-0.95 for intradialytic hypotension prediction; C-indices up to 0.83 for mortality prediction; AUROC ≈ 0.96 for vascular access monitoring

• Primary Outcome: AI/ML applications demonstrate promise across five domains: prediction/prognosis, CKD progression detection, clinical decision support, vascular access monitoring, and NLP applications

• Nnt Or Sensitivity: Sensitivity and specificity values not systematically reported across applications

• Confidence Interval: Not reported

Clinical Application

Limited by data privacy concerns, model interpretability issues, regulatory complexity, and need for diverse datasets Relevant to Australian nephrology practice; would require TGA approval for clinical decision support tools; potential integration with existing electronic health records in public and private dialysis units Patients receiving haemodialysis, peritoneal dialysis, and those at risk of CKD progression requiring renal replacement therapy

Abstract

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.

References

1. 1.David-Olawade, C., Ogunbona, A., Olawuyi, M. O., Makanjuola, O. F., Alabi, B. D., Olawade, J. O., & Olawade, D. B. (2026). Artificial intelligence and machine learning applications in dialysis: Current applications, challenges, and future directions. Clinica Chimica Acta, 120908. https://doi.org/10.1016/j.cca.2026.120908