CardioTwin-XAI: A Consumer-Centric Digital Twin Framework for Predictive Risk Stratification and Personalized Management of Coronary Artery Disease in Healthcare 5.0

Abstract

Consumer-Centric Digital Twins (CCDT) are emerging as transformative enablers in Healthcare 5.0, offering real-time monitoring, predictive analytics, and patient-specific treatment optimization. This paper introduces CardioTwin-XAI, a novel AI-driven CCDT framework designed for early detection, risk prediction, and personalized management of Coronary Artery Disease (CAD) and related conditions such as atherosclerosis, myocardial infarction (MI), and heart failure (HF). The proposed system integrates IoT-enabled wearable sensors, continuous hemodynamic profiling, and federated multimodal learning to build a dynamic, patient-specific digital replica. The key innovation of CardioTwin-XAI is its unified integration of explainable AI (XAI) with privacy-preserving federated learning, wherein interpretable attribution mechanisms are embedded within distributed model updates, enabling transparent predictions without compromising data confidentiality across healthcare institutions. A hybrid transformer-GNN architecture captures complex spatiotemporal cardiovascular dynamics, while differential privacy and blockchain-based data sharing ensure regulatory compliance. Evaluation on multi-institutional datasets demonstrates 92.6% accuracy in CAD prediction, 31% improvement in treatment planning efficiency, and 40% reduction in unnecessary interventions. The proposed framework paves the way for scalable, secure, and explainable cardiovascular care in next-generation healthcare ecosystems.

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