Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719 [DOI: 10.5527/wjn.v15.i2.117719]
Corresponding Author of This Article
Kirolos Eskandar, MD, Researcher, Medicine and Surgery, Helwan University, Al Masaken Al Iqtisadeyah, Helwan, Cairo Governorate 4034572, Giza 11795, Al Jīzah, Egypt. kirolos210575@med.helwan.edu.eg
Research Domain of This Article
Urology & Nephrology
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review-article
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Eskandar K. Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies. World J Nephrol 2026; 15(2): 117719 [DOI: 10.5527/wjn.v15.i2.117719]
World J Nephrol. Jun 25, 2026; 15(2): 117719 Published online Jun 25, 2026. doi: 10.5527/wjn.v15.i2.117719
Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies
Kirolos Eskandar
Kirolos Eskandar, Medicine and Surgery, Helwan University, Giza 11795, Al Jīzah, Egypt
Author contributions: Eskandar K conceptualized and designed the study, created the artwork, supervised, made critical revisions, conducted the literature review, did the analysis, interpretation of data, and drafted the original manuscript, prepared the draft, and approved the submitted version.
AI contribution statement: AI tools were not involved in the study design or interpretation of results and no part of the manuscript was generated by AI. AI tools were used solely for language polishing and readability improvement. No AI-generated images were used.
Conflict-of-interest statement: The author declares no conflict of interests for this article.
Corresponding author: Kirolos Eskandar, MD, Researcher, Medicine and Surgery, Helwan University, Al Masaken Al Iqtisadeyah, Helwan, Cairo Governorate 4034572, Giza 11795, Al Jīzah, Egypt. kirolos210575@med.helwan.edu.eg
Received: December 16, 2025 Revised: January 18, 2026 Accepted: February 9, 2026 Published online: June 25, 2026 Processing time: 183 Days and 11.6 Hours
Core Tip
Core Tip: Artificial intelligence (AI) holds significant promise for transforming chronic kidney disease (CKD) care by integrating longitudinal, high-dimensional, and multimodal clinical data to support earlier detection, improved risk stratification, and personalized management. This review synthesizes recent evidence on AI applications across the CKD continuum, including early detection, risk prediction, personalized treatment, complication management, dialysis, and kidney transplantation. Beyond reporting model performance, it critically examines validation quality, calibration, explainability, equity, and real-world implementation barriers, highlighting the persistent gap between methodological innovation and routine clinical deployment. The review outlines key priorities required to translate AI tools into safe, equitable, and clinically meaningful CKD care.
