Cross-fusion of digital twins and artificial intelligence in diabetes: from mechanistic elucidation to full-cycle precision management

Abstract

The integration of digital twins (DT) and artificial intelligence (AI) is driving a paradigm shift in diabetes mellitus (DM) care from traditional population-based, symptomatic intervention to a lifecycle-oriented precision management approach. This review systematically elaborates on the cross-disciplinary integration mechanisms of the two technologies. By integrating multimodal data and constructing physiologically constrained hybrid models, it achieves multi-scale mechanistic analyses ranging from single-cell β-cell protection to multi-organ complications at the basic research level. On the clinical application front, it not only significantly enhances the early screening sensitivity for diabetic retinopathy and the accuracy of blood glucose prediction, but also optimizes insulin dosing, personalized nutritional plans, and exercise decision support through virtual trials. In the field of drug development, virtual clinical trials accelerate target discovery and drug repurposing. Although substantial technical, regulatory, and ethical challenges remain unresolved, ongoing progress in hybrid modeling, federated learning, explainable AI, and evolving regulatory frameworks provides a plausible pathway toward individualized prediction and proactive management, provided that claims of clinical readiness are matched by rigorous prospective validation.