Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies

doi:10.5527/wjn.v15.i2.117719

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World Journal of Nephrology

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2220-6124

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Nephrol. Jun 25, 2026; 15(2): 117719
Published online Jun 25, 2026. doi: 10.5527/wjn.v15.i2.117719

Table 8 Key barriers to clinical implementation of artificial intelligence in chronic kidney disease and proposed mitigation strategies

Barrier category	Failure mechanism	Quantitative evidence	Mitigation
Technical	Overfitting, drift, miscalibration	AUROC drop 0.05-0.15 on external validation	Multisite validation, recalibration
Organizational	Poor EHR/workflow integration	40%-60% of deployment cost/time	FHIR-based integration, co-design
Regulatory	Extended approval timelines	Months-years added to development	Early regulatory planning
LMIC context	Infrastructure and data gaps	Limited cost-effectiveness evidence	Local retraining, staged rollout

AUROC: Area under the receiver operating characteristic curve; LMIC: Low- and middle-income countries; EHR: Electronic health records; FHIR: Fast healthcare interoperability resources.

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Citation: Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719
URL: https://www.wjgnet.com/2220-6124/full/v15/i2/117719.htm
DOI: https://dx.doi.org/10.5527/wjn.v15.i2.117719

Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719 [PMID: 42395685 DOI: 10.5527/wjn.v15.i2.117719]

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