Artificial intelligence for precision nephrology: current evidence to clinical translation and future prospects

Background and objectives Artificial intelligence (AI) is increasingly being applied across nephrology, with potential applications spanning disease prediction, diagnosis, treatment optimisation, and clinical decision support. However, reported model performance varies substantially across clinical domains, and high predictive performance does not necessarily demonstrate clinical utility or readiness for deployment. This narrative review synthesizes current evidence on the applications of AI in nephrology and critically evaluates the methodological maturity, validation, interpretability, fairness, and translational readiness of AI systems across major nephrology domains. Methods A structured literature search of PubMed was conducted using terms related to AI and nephrology, with the primary eligibility period restricted to studies published from 2020 to 2025. The search strategy incorporated artificial intelligence, machine learning, deep learning, neural networks, natural language processing, large language models, generative AI, computer vision, reinforcement learning, federated learning, and related terms, together with nephrology and kidney-disease terms. Title/abstract and full-text screening were performed independently by the first and second authors, with disagreements resolved by the third author. Two foundational pre-2020 studies identified through citation chaining were retained because of their importance to the subsequent development of AI applications in nephrology. Evidence was synthesized qualitatively according to study design, validation, predictive performance, interpretability, fairness, prospective evaluation, and translational readiness. Results AI applications were identified across acute kidney injury, chronic kidney disease, nephropathology, kidney transplantation, haemodialysis, peritoneal dialysis, large language models, reinforcement learning, and federated learning. Across these domains, many models demonstrated promising discrimination, but evidence maturity varied considerably. External validation was present for some applications, whereas prospective evaluation and evidence of clinical utility remained limited. The literature also highlighted challenges related to calibration, generalizability, interpretability, algorithmic fairness, data governance, and integration into clinical workflows. Large language models and federated learning represent emerging areas, but nephrology-specific evidence remains comparatively limited. Conclusions AI has substantial potential to support precision nephrology, but technical performance should not be equated with clinical effectiveness or deployment readiness. Future research should prioritize external and prospective validation, assessment of clinical utility and patient outcomes, transparent reporting, fairness, regulatory oversight, and effective human–AI integration.