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 9 Anticipated future directions of artificial intelligence in chronic kidney disease care across time horizons

Time horizon	Direction	Evidence base	Primary clinical impact
5 years	Multimodal foundation models	Rapid growth in medical multimodal AI[125]	Improved calibration and consistency
	Wearables and continuous monitoring	Digital CKD health reviews[126]	Earlier detection between visits
	PRO and text integration	41% of CKD AI studies use unstructured data[7]	Patient-centered decision support
≥ 10 years	Digital twins	Early CKD prototype models[7]	In silico therapy planning
	Multi-omics AI	Precision medicine frameworks[127]	Target discovery and stratification

PRO: Patient-reported outcomes; CKD: Chronic kidney disease; AI: Artificial intelligence.

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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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