Generative Artificial Intelligence and Digital Twin Ecosystems: A Standardization-Aligned Framework for Precision Healthcare and Industrial Cyber-Physical Resilience
Keywords:
Digital Twin, Generative AI, Cyber-Physical Systems, Precision HealthcareAbstract
The intersection of generative artificial intelligence (GenAI) and digital twin technology represents a transformative frontier in the operational management of complex cyber-physical systems (CPS). As industrial and healthcare infrastructures become increasingly reliant on the Internet of Medical Things (IoMT) and high-velocity sensor data, the requirement for real-time, high-fidelity virtual replicas has become critical. This article proposes a novel, standardization-aligned framework that integrates generative AI-driven sensor fusion with decentralized federated learning to enhance the security, interpretability, and fault tolerance of digital twin ecosystems. By analyzing the transition from traditional, deterministic monitoring to adaptive, predictive modeling, this study addresses the challenges of data sparsity, adversarial vulnerability, and the necessity for explainable machine learning in high-stakes environments such as cardiology and oncology. Through a comprehensive synthesis of current literature, the research elaborates on the mechanisms of multi-fidelity data resampling and concurrent end-to-end synchronization, offering a robust architecture that facilitates seamless interoperability across heterogeneous platforms. The findings suggest that by embedding generative intelligence within a modular, security-aware edge computing infrastructure, organizations can unlock unprecedented levels of precision while maintaining rigorous compliance with evolving international standards for cybersecurity and patient data privacy.
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