Digital Twin-Driven Risk Assessment and Security Optimization of Large Language Model Agents in Personalized Healthcare

Abstract

The integration of large language model agents into personalized healthcare promises transformative improvements in clinical decision support, patient engagement, and treatment customization. However, these autonomous software entities introduce unprecedented risks stemming from adversarial manipulation, data biases, and systemic failures that can compromise patient safety and privacy. This paper presents a novel framework that leverages digital twin technology to systematically assess and optimize the security profile of LLM agents operating in healthcare environments. The digital twin constructs a high-fidelity virtual replica of the agent, its interaction context, and the underlying care ecosystem, enabling continuous simulation of threat scenarios without endangering real patients. Through a dual-loop architecture, risk assessment outputs inform proactive security optimization, while the optimized configurations are fed back into the digital twin for validation. We examine the structural trade-offs between fidelity, computational cost, and real-time responsiveness, and discuss the architectural requirements for integrating digital twin pipelines with clinical data infrastructures. The governance implications of such simulation-driven risk management are analyzed, including questions of regulatory accountability, cross-institutional data sharing, and algorithmic fairness. By situating the discussion at the system level, we avoid narrow technical fixes and instead advocate for a holistic socio-technical approach that treats LLM agent security as an emergent property of continuous, evidence-based co-adaptation between the digital and physical realms. The paper concludes with a forward-looking perspective on how federated digital twin ecosystems could underpin a new class of resilient healthcare AI infrastructures, balancing innovation with rigorous safety guarantees.