AI-Driven Digital Twin Architecture for Healthcare Infrastructure Resilience

Abstract

The rapid growth of digital technologies, connected medical devices, cloud-based healthcare platforms, and intelligent clinical systems has increased the complexity of healthcare infrastructure. Although these technologies improve patient care and operational efficiency, healthcare organizations continue to face challenges such as equipment failures, cyber threats, resource shortages, patient surges, and operational disruptions. Traditional maintenance and monitoring methods are often inadequate for real-time infrastructure management. Artificial Intelligence (AI) and Digital Twin technologies provide an effective solution by enabling real-time monitoring, predictive maintenance, resource optimization, and intelligent decision support. A Digital Twin creates a virtual model of healthcare infrastructure that continuously synchronizes with data from IoT devices, electronic health records, hospital systems, and medical equipment. This paper proposes an AI-powered Digital Twin architecture consisting of four modules: Healthcare Data Acquisition and Integration, Digital Twin Modeling, AI-Based Predictive Analytics, and Infrastructure Optimization. The framework analyzes real-time data to predict equipment failures, optimize maintenance, assess infrastructure risks, and improve operational resilience through continuous synchronization between physical and virtual healthcare environments. Performance is evaluated using metrics such as prediction accuracy, infrastructure availability, resource utilization, maintenance efficiency, response time, and operational continuity. Experimental results demonstrate that the proposed framework improves failure prediction, reduces downtime, optimizes resource allocation, and enhances emergency response compared to conventional approaches. Overall, the architecture supports the development of intelligent, resilient, and sustainable healthcare systems aligned with Industry 5.0 principles.