Digital twins in healthcare: a comprehensive review and future directions

Abstract

Digital Twin (DT) technology has emerged as a transformative force in healthcare, offering unprecedented opportunities for personalized medicine, treatment optimization, and disease prevention. This comprehensive review examines the current state of DTs in healthcare, analyzing their implementation across different physiological levels-from cellular to whole-body systems. We systematically review the latest developments, methodologies, and applications while identifying challenges and opportunities. Our analysis encompasses technical frameworks for cardiovascular, neurological, respiratory, metabolic, hepatic, oncological, and cellular DTs, highlighting significant achievements such as population-scale cardiac modeling (3,461 patient cohort), reduced atrial fibrillation recurrence rates through patient-specific cardiac models, improved brain tumor radiotherapy planning, advanced liver regeneration modeling with real-time simulation capabilities, and enhanced glucose management in diabetes. We detail the methodological foundations supporting different DT implementations, including data acquisition strategies, physics-based modeling approaches, statistical learning algorithms, neural network-based control systems, and emerging artificial intelligence techniques. While discussing implementation challenges related to data quality, computational constraints, and validation requirements, we provide a forward-looking perspective on future opportunities for enhanced personalization, expanded application areas, and integration with emerging technologies. This review offers a multidimensional assessment of healthcare DTs and outlines future directions for their development and integration. This review demonstrates that while healthcare DTs have achieved remarkable clinical successes-from reducing cardiac arrhythmia recurrence rates by over 13% to enabling 97% accuracy in neurodegenerative disease prediction, and achieving sub-millisecond liver response predictions with high accuracy-their clinical translation requires addressing challenges such as data integration, computational scalability, digital equity, and validation frameworks.

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