Review Assesses AI Evidence in Nephrology

A 2026 narrative review in Frontiers in Nephrology examines evidence on artificial intelligence (AI) in kidney care, including disease prediction, diagnosis, treatment optimization and clinical decision support. It says reported model performance varies widely by clinical area, and strong predictive performance alone does not establish clinical usefulness or readiness for deployment. The review assesses methodological maturity, validation, interpretability, fairness and readiness to move into practice.

The authors searched PubMed for studies published from 2020 to 2025 using terms covering AI methods and kidney disease. They screened titles, abstracts and full texts independently, resolving disagreements through a third author. Two foundational studies published before 2020 were also included because of their importance to later work. Evidence was assessed qualitatively by study design, validation, predictive performance, interpretability, fairness, prospective evaluation and readiness for clinical use.

The review found AI applications across acute and chronic kidney disease, kidney pathology and transplantation, haemodialysis, peritoneal dialysis, large language models, reinforcement learning and federated learning. Many models showed promising ability to distinguish between outcomes, but the strength of evidence differed across areas. Some applications had external validation, while prospective evaluation and evidence of clinical benefit remained limited. The literature also identified challenges in calibration, generalizability, interpretability, algorithmic fairness, data governance and integration into clinical workflows. Evidence for large language models and federated learning in nephrology was comparatively limited.

The authors say AI could support precision nephrology, but technical performance should not be treated as proof of clinical effectiveness or deployment readiness. They call for external and prospective validation, evaluation of clinical utility and patient outcomes, transparent reporting, fairness, regulatory oversight and effective human–AI integration.

A 2026 narrative review in Frontiers in Nephrology examines evidence on artificial intelligence (AI) in kidney care, including disease prediction, diagnosis, treatment optimization and clinical decision support. It says reported model performance varies widely by clinical area, and strong predictive performance alone does not establish clinical usefulness or readiness for deployment. The review assesses methodological maturity, validation, interpretability, fairness and readiness to move into practice.

The authors searched PubMed for studies published from 2020 to 2025 using terms covering AI methods and kidney disease. They screened titles, abstracts and full texts independently, resolving disagreements through a third author. Two foundational studies published before 2020 were also included because of their importance to later work. Evidence was assessed qualitatively by study design, validation, predictive performance, interpretability, fairness, prospective evaluation and readiness for clinical use.

The review found AI applications across acute and chronic kidney disease, kidney pathology and transplantation, haemodialysis, peritoneal dialysis, large language models, reinforcement learning and federated learning. Many models showed promising ability to distinguish between outcomes, but the strength of evidence differed across areas. Some applications had external validation, while prospective evaluation and evidence of clinical benefit remained limited. The literature also identified challenges in calibration, generalizability, interpretability, algorithmic fairness, data governance and integration into clinical workflows. Evidence for large language models and federated learning in nephrology was comparatively limited.

The authors say AI could support precision nephrology, but technical performance should not be treated as proof of clinical effectiveness or deployment readiness. They call for external and prospective validation, evaluation of clinical utility and patient outcomes, transparent reporting, fairness, regulatory oversight and effective human–AI integration.