Diabetes, metabolic syndrome and obesity : targets and therapy · 2026
Explainable Artificial Intelligence for Early Diabetic Kidney Disease Risk Profiling in Type 2 Diabetes: Translational Readiness and Clinical Boundaries.
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Wu Tianqiang, Cai Wenpin, Guan Zihan, Li Zhixiang
Аннотация
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.
RESULTS: Evidence was organized by clinical task: occult or early diabetic kidney disease detection, incident kidney disease prediction, progression and referral forecasting, retinal and multimodal profiling, and equity-aware risk assessment. Current studies show promising discrimination and increasing use of explanation methods, calibration, decision-curve analysis, web tools, and external or temporal validation. Persistent limits include heterogeneous endpoints, diagnostic circularity, uneven external validation, sparse workflow evidence, and limited fairness or uncertainty reporting.
CONCLUSION: Explainable artificial intelligence is best viewed as a risk-stratification, communication, and audit layer within guideline-based care, not as a standalone diagnostic or treatment-decision system.
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.
RESULTS: Evidence was organized by clinical task: occult or early diabetic kidney disease detection, incident kidney disease prediction, progression and referral forecasting, retinal and multimodal profiling, and equity-aware risk assessment. Current studies show promising discrimination and increasing use of explanation methods, calibration, decision-curve analysis, web tools, and external or temporal validation. Persistent limits include heterogeneous endpoints, diagnostic circularity, uneven external validation, sparse workflow evidence, and limited fairness or uncertainty reporting.
CONCLUSION: Explainable artificial intelligence is best viewed as a risk-stratification, communication, and audit layer within guideline-based care, not as a standalone diagnostic or treatment-decision system.