Patient-Specific Digital Twins for Personalized Healthcare: A Hybrid AI and Simulation-Based Framework

Digital twins (DTs) represent a transformative paradigm in personalized medicine, enabling real-time, patient-specific simulations that support precision diagnosis, continuous monitoring, and adaptive treatment planning. This paper presents a novel hybrid framework that integrates mechanistic physiological modeling with deep learning to construct patient-specific digital twins. The proposed architecture couples a dynamic simulation engine—capable of modeling organ-system interactions—with neural encoder-decoder networks that extract latent representations from heterogeneous clinical data sources, including biosignals (e.g., ECG), medical imaging, and wearable sensor streams. A probabilistic learning module further enables predictive adaptation under uncertainty, leveraging Bayesian inference and reinforcement learning. The framework supports continuous synchronization between a patient’s real-world physiological state and its digital counterpart, enabling individualized risk assessment, disease trajectory forecasting, and therapy simulation. Extensive experiments on large-scale benchmark datasets (MIMIC-IV and PhysioNet) demonstrate that our hybrid approach significantly outperforms conventional machine learning and standalone AI models across multiple clinical prediction tasks. By fusing physiological realism with data-driven intelligence, the proposed digital twin framework offers a scalable, interpretable, and clinically actionable foundation for next-generation precision healthcare systems.