From assistant to collaborator: A systematic review of the evolution of artificial intelligence in end-stage renal disease care and management.

Abstract

We aimed to systematically analyze the historical evolution of artificial intelligence (AI) in end-stage renal disease (ESRD) management and propose a developmental framework to map its progression from assistive tools to cognitive collaborators. A systematic review was conducted following PRISMA 2020 guidelines, identifying 100 eligible studies from PubMed, IEEE Xplore, and Web of Science. A three-stage analytical framework was applied to chart the technological evolution of AI in ESRD care: (1) rule-based assistive tools, (2) data-driven learning systems, and (3) emerging large language model- and agent-based cognitive systems. A total of 100 studies were included, all focusing on patients with end-stage renal disease managed through hemodialysis, peritoneal dialysis, or kidney transplantation. Three primary application domains were identified: risk prediction (49.0%), diagnostic support (25.0%), and monitoring and management (26.0%). The analytical framework revealed a developmental progression from interpretable rule-based systems (Stage 1) to high-performing data-driven models (Stage 2), which achieve clinically relevant metrics (e.g., area under the receiver operating characteristic curve [AUC] 0.80-0.90) but often lack external validation. Emerging large language model- and agent-based systems (Stage 3) demonstrate notable versatility but introduce new challenges related to reliability, factual accuracy, and safety alignment. The proposed three-stage framework clarifies AI's technological and functional evolution in ESRD care. This perspective highlights a critical need to bridge the gap between high-performance modeling and validated clinical utility. Future work should focus on robust external validation and the development of frameworks for the safe, reliable, and ethical deployment of next-generation cognitive AI agents.