The late diagnosis of chronic kidney disease (CKD) is a global problem that limits the opportunity to initiate disease-modifying therapies. Artificial intelligence approaches using imaging or laboratory-based models can facilitate the early detection and risk stratification of CKD and thereby enable optimal treatment to reduce the burden of the disease.
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N.T. reports consultancy agreements with Marizyme, Mesentech, PulseData and Renibus; ownership interests in ClinPredict, Klinrisk, Marizyme, Mesentech, PulseData, Quanta and Renibus; receiving research funding from AstraZeneca, Bayer, BI-Lilly, Janssen and Otsuka; receiving honoraria from AstraZeneca, Bayer, BI-Lilly, Janssen, Otsuka Pharmaceuticals and Pfizer; having patents or royalties with Klinrisk and Marizyme; having an advisory or leadership role with ClinPredict and Klinrisk; other interests or relationships with the National Kidney Foundation; and being the founder of ClinPredict and Klinrisk. C.S. reports no competing interests.
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Tangri, N., Sabanayagam, C. Artificial intelligence approaches to enable early detection of CKD. Nat Rev Nephrol 21, 153–154 (2025). https://doi.org/10.1038/s41581-025-00933-6
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DOI: https://doi.org/10.1038/s41581-025-00933-6
