A Blockchain-Enabled Consumer-Centric Digital Twin Framework with AI-Driven Predictive Analytics for Healthcare 5.0

Abstract

Digital twins are redefining healthcare by enabling virtual representations of patients that support continuous monitoring, simulation, and personalized treatment. However, challenges, such as fragmented data management, delayed synchronization, and a lack of coordination among healthcare entities, limit their practical use. To address these challenges, this article proposes a blockchain-enabled Consumer-Centric Digital Twin (CCDT) framework that integrates transparent data sharing, smart-contract automation, and intelligent analytics. The proposed system consists of multiple entities, including the Trusted Authority (TA), Patient (P), Healthcare Provider (HP), Edge Gateway (EG), Blockchain (BC), and InterPlanetary File System (IPFS), which interact through smart contracts for registration, data validation, access control, and synchronization. Here, a lightweight Artificial Intelligence (AI) module embedded within the Digital Twin (DT) performs predictive diagnosis and adaptive learning using validated physiological data. The framework is evaluated using different AI metrics, namely accuracy, precision, recall, and F1-score, including blockchain metrics, such as transaction latency, throughput, energy consumption, and gas cost. The results of the experiments demonstrate that the proposed model achieves efficient synchronization, reliable data handling, and scalable computation suitable for continuous patient monitoring.

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