AI-Driven Digital Twins: Real-Time Multimodal Data Integration for Personalized Therapeutic Optimization in Healthcare

This paper proposes an AI-driven digital twin (DT) framework for personalized therapeutic optimization by inte- grating real-time multimodal data from electronic health records (EHRs), wearable devices, genomic sequencing, and environmen- tal sensors. The framework employs a four-layer architecture- data ingestion, unified processing, simulation, and visualization-to address interoperability challenges through FHIR standards and blockchain-based data provenance. Leveraging federated learn- ing for privacy-preserving model training and physics-informed neural networks (PINNs) for biophysical simulations, the system enables dynamic prediction of treatment outcomes and closed- loop therapy adjustment via reinforcement learning. Case studies in oncology (triple-negative breast cancer) and cardiology (heart failure) demonstrate 30–40 % improvement in treatment efficacy, with chemotherapy resistance predicted at 92% accuracy and a 40% reduction in hospital readmissions through early anomaly detection. Challenges such as computational scalability, ethical data governance, and clinician-AI collaboration are discussed, alongside actionable recommendations for integrating digital twins into clinical workflows. This work bridges the gap between reactive and proactive healthcare, offering a scalable pathway for precision medicine.