AI-Enabled Digital Twin-Based Chronic Disease Management: Personalized Prediction and Treatment Optimization for Diabetes, Cardiovascular Disease, and Cancer

Abstract

Chronic diseases including diabetes, cardiovascular disease (CVD), and cancer represent the leading causes of mortality and morbidity worldwide, demanding continuous, personalized, and adaptive management strategies. Digital twin (DT) technology—virtual, data-driven replicas of individual patients continuously synchronized with real-time physiological data—has emerged as a transformative paradigm for precision medicine. This paper presents an AI-enabled digital twin framework for chronic disease management that integrates mechanistic physiological models, machine learning algorithms, and multimodal patient data to enable personalized prediction and treatment optimization. We develop a hybrid architecture combining deep reinforcement learning for adaptive therapy control, Gaussian process regression for uncertainty-aware risk prediction, and physics-informed neural networks for simulating disease progression. Mathematical formulations for the glucose-insulin dynamics, cardiac electrophysiology, and tumor growth modeling are presented within a unified DT framework. Experimental evaluation across three disease domains demonstrates that the proposed system achieves a glucose prediction RMSE of 12.4 mg/dL (vs. 21.7 mg/dL for conventional models), arrhythmia risk classification AUC of 0.89, and cancer treatment response prediction accuracy of 84.3%. The DT-based treatment optimization reduced simulated hypoglycemic events by 67% and improved time-in-range by 22% compared to standard protocols. These results indicate that AI-enabled digital twins can significantly enhance personalized chronic disease management, though challenges in data integration, clinical validation, and ethical implementation remain.