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
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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
Abstract
Chronic kidney disease (CKD) is a heterogeneous and frequently underdiagnosed condition in which conventional diagnostic and prognostic approaches based on static biomarkers and linear risk models offer limited sensitivity for early detection and individualized risk stratification. Artificial intelligence (AI) enables integration of longitudinal, high-dimensional, and multimodal clinical data, providing opportunities to address key limitations in current CKD care. This narrative review synthesizes human clinical studies published between 2019 and 2025 evaluating AI applications across the CKD continuum, including early detection, risk prediction, disease progression, treatment personalization, complication management, dialysis, and kidney transplantation. Evidence was analyzed thematically with emphasis on model performance, validation strategies, explainability, and translational readiness. Across multiple domains, AI-based models generally demonstrated improved discrimination and risk stratification compared with traditional statistical approaches, particularly when incorporating longitudinal trajectories and multimodal inputs. However, most models were exploratory or internally validated, with limited external validation and minimal prospective evaluation. Reported applications included earlier CKD identification, improved prediction of progression and acute kidney injury, optimization of pharmacologic and dialysis strategies, and enhanced prognostication in kidney transplantation. Overall, AI holds promise for more precise CKD detection and management, but real-world impact will require prospective validation, standardized reporting, and rigorous assessment of clinical utility.
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.
