Digital Twin-Based Personalized Physiological Monitoring and Health Risk Prediction
Keywords:
digital twin; physiological monitoring; health risk prediction; personalized medicine; system architecture; governance; artificial intelligence; health data infrastructureAbstract
Personalized physiological monitoring is transitioning from intermittent clinical observation toward continuous, context-aware, and predictive health management. Digital twin technologies offer a promising architectural paradigm for integrating heterogeneous physiological data streams with computational models of individual patients. This paper presents a system-level analysis of digital twin-based personalized physiological monitoring and health risk prediction. It examines the structural trade-offs involved in building adaptive digital twin systems, including synchronization between physical and virtual states, edge-cloud distribution, model fidelity, and data governance. The discussion emphasizes architectural layering, uncertainty-aware inference, robustness under real-world sensing conditions, and the need for fairness and accountability in longitudinal predictive systems. The paper evaluates the implications of digital twin deployment in clinical and public health infrastructures and considers regulatory, ethical, and sustainability dimensions. Rather than proposing a single computational model, the study articulates how personalized physiological monitoring can be designed as a socio-technical infrastructure that balances predictive performance, interpretability, operational feasibility, and equitable access. The analysis draws on recent advances in digital twin engineering, artificial intelligence in medicine, and health data governance to outline a forward-looking research agenda. The conclusion highlights that sustainable digital twin deployment requires co-design across sensing, modeling, governance, and clinical workflow rather than isolated algorithmic optimization.
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