LLM-Twins: A Large Language Model driven trusted Cyber Twin framework for Healthcare 5.0 ecosystems

Healthcare 5.0 leverages advanced models and artificial intelligence (AI) analytics to address complex health challenges. With the rise of digital twins in industrial automation, the focus has shifted toward creating cyber twins (CT) models that simulate patient conditions, reducing the risk of misdiagnosis on actual patients. In our proposed framework, patient data from real-time sensors, wearables, and electronic health records (EHRs) are collected and securely stored across decentralized personal web servers using Solid PoDs. We connect the Solid PoDs via blockchain that ensures that data remains immutable and accessible only to authenticated stakeholders via robust access control matrices. The core of the framework uses large language models (LLMs) in the CT design process. The LLM enhances the system by interpreting heterogeneous data streams, providing real-time natural language summaries, and supporting decision-making through intelligent analytics. The LLM cognitive layer aids in verifying and optimizing chaincode operations within the blockchain framework. Comparative analysis against conventional methods, such as IPFS, demonstrates that LLM-Twins not only preserves data privacy and integrity but also significantly reduces latency in CT creation.