Artificial Intelligence–Driven Prediction and Management of Chronic Diseases: A Comparative Analysis of Diabetes, Cardiovascular Disease, and Cancer

Abstract

Artificial intelligence (AI) is increasingly used to convert heterogeneous clinical data into actionable predictions across chronic disease pathways, yet the maturity and clinical role of AI differ substantially among diabetes, cardiovascular disease (CVD), and cancer. This structured comparative evidence synthesis evaluated human studies and high-quality reviews addressing AI-enabled risk prediction, diagnosis, monitoring, complication forecasting, and management in these three disease domains. PubMed/MEDLINE, relevant publisher sources, and cross-referenced literature were examined up to September 2026. Animal experiments, in vivo studies, wet-laboratory-only investigations, and reports without a clinically relevant AI endpoint were excluded. Thirty-six core publications were retained for comparative synthesis. Diabetes showed its strongest implementation profile in continuous glucose monitoring, hypoglycemia forecasting, complication prediction, and automated insulin delivery; external validation remains a recurring weakness in complication models. CVD demonstrated broad AI utility across electrocardiography, imaging, wearable monitoring, and time-to-event risk prediction, with randomized evidence emerging for AI-supported echocardiographic workflows. Cancer showed the greatest dependence on high-dimensional imaging, digital pathology, and multi-omics integration; AI-assisted radiology and pathology can improve diagnostic performance, but generalizability and risk of bias remain major concerns. Across the three diseases, multimodal integration usually increased predictive capacity, whereas clinical translation was constrained by dataset shift, incomplete calibration, limited prospective validation, variable interpretability, privacy concerns, and under-reporting of fairness. A five-domain evidence-maturity framework indicated relatively mature continuous-management applications in diabetes, signal- and imaging-based prediction in CVD, and imaging/pathology plus multi-omics applications in cancer. The comparative findings support disease-specific deployment rather than a single universal AI strategy. Clinically useful AI should be externally validated, calibrated, monitored after deployment, integrated with human decision-making, and reported using contemporary AI-specific standards.