aceWhen machines estimate kidney function and predict risk: Clinical applications of machine learning
The use of artificial intelligence, and particularly machine learning, is rapidly expanding in medicine. In nephrology, these approaches have generated growing interest owing to the availability of large clinical, biological, and administrative datasets, as well as the need for more accurate assessment of kidney function and changes over time. Two major areas of application have emerged: estimation of glomerular filtration rate (GFR) and prediction of chronic kidney disease (CKD) progression. Initial studies foc…
Large multicenter studies show machine learning improves kidney function estimation only marginally over EKFC or CKD-EPI, and few risk models clearly beat the Kidney Failure Risk Equation.
Only a limited number of progression models have shown a clear clinical advantage over established tools such as KFRE, so the practical value of machine learning in nephrology remains unproven.
Evidence
- Peer-reviewedNéphrologie & Thérapeutique2026-10-27
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Truvace Impact Record TRV-2026-2293, v1: “When machines estimate kidney function and predict risk: Clinical applications of machine learning.” Truvace, 2026-10-28. /record/TRV-2026-2293 (accessed at citation time). sha256 520e1398144e6f8a…
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