Artificial intelligence in chronic kidney disease: Early detection, risk prediction, and personalized treatment strategies
Year: 2026
Type: article
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.
Source: World Journal of Nephrology
Author: Kirolos Eskandar
Institution: Helwan University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Awards: G1707367334, G4754062871
OpenAlex ID: W7164743780