Diabetes, metabolic syndrome and obesity : targets and therapy
Explainable Artificial Intelligence for Early Diabetic Kidney Disease Risk Profiling in Type 2 Diabetes: Translational Readiness and Clinical Boundaries
By Wu T, Cai W, Guan Z and 1 others.
Abstract
Purpose
Early kidney-risk profiling in type 2 diabetes is difficult because albuminuria, estimated glomerular filtration rate, comorbidity, and care access capture different aspects of diabetic kidney disease. This review examines where explainable artificial intelligence may support clinically defensible kidney-risk profiling.
Methods
This narrative review used structured searches of PubMed, Semantic Scholar, and OpenAlex using kidney, diabetes, and artificial intelligence/explainability terms, supplemented by citation chasing and full-text assessment of key records. Broad query returns were documented for PubMed (600) and Semantic Scholar (956). Records were screened for topic fit and prioritized for clinical relevance to kidney-risk tasks, endpoint validity, explainability, validation, calibration, missing-data handling, fairness, and workflow relevance; 18 clinically relevant evidence records were retained for detailed synthesis in Supplementary Table 1.
