Agentic AI-Driven bio-digital twins: A self-adaptive framework for personalized disease prediction and therapy design

Personalized healthcare has seen remarkable advancements through digital twin technologies, yet current systems lack real-time decision-making and self-adaptation capabilities essential for dynamic biological processes. Integrating Agentic AI offers a promising pathway to overcome the limitations of static modeling by enabling autonomous, goal-driven health interventions. This study proposes a novel Agentic AI-Driven Bio-Digital Twin framework that continuously evolves using real-time biological, behavioral, and environmental data to deliver early disease prediction, treatment optimization, and adaptive therapy design for individual patients. The proposed system utilizes a hybrid Agentic AI engine combining reinforcement learning and transformer-based modeling to simulate, monitor, and adapt biological processes. A multi-layered bio-digital twin architecture is developed, comprising a synthetic biology layer (for simulating gene-protein-pathway interactions), a physiological data layer (sensor-driven inputs), and an Agentic decision layer (goal-aware adaptation). The framework is validated using synthetic datasets emulating Type 2 diabetes progression and wearable sensor data (ECG, glucose, activity levels). Experimental results demonstrate a 92.4% accuracy in early disease detection and a 38% improvement in therapy plan personalization compared to rule-based systems. The Agentic AI layer achieved a 27% faster adaptation time to physiological changes, significantly outperforming baseline models. The system dynamically proposed individualized treatment options aligned with simulated genomic variations and behavioral patterns. The Agentic AI-driven bio-digital twin framework represents a significant step toward real-time, personalized, and autonomous healthcare delivery. By integrating synthetic biology simulation with self-adaptive AI, the model offers a scalable and intelligent solution for future precision medicine applications.