AI-Augmented Digital Twins for Personalized Patient Monitoring: A Novel Framework for Predictive and Adaptive Healthcare

Abstract

Digital twins, traditionally applied in industrial domains, are now emerging as a transformative force in personalized healthcare. This paper introduces a novel framework that integrates digital twin technology with artificial intelligence (AI) to enable real-time, adaptive, and predictive patient monitoring. The proposed system continuously ingests multimodal data streams—ranging from wearable sensors to electronic health records—to construct an evolving virtual replica of a patient’s physiological state. Leveraging deep sequence modeling, multi-modal data fusion, and reinforcement learning, the framework forecasts adverse health events, simulates personalized treatment pathways, and dynamically refines care recommendations based on real-world feedback. Evaluations conducted on the MIMIC-III dataset and a real-time pilot study demonstrate substantial improvements in prediction accuracy, alert latency, and intervention efficacy over existing models. These findings suggest that AI-augmented digital twins can provide a robust foundation for proactive, data-driven, and individualized healthcare delivery.

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