Background: Digital twin (DT) technology — the dynamic virtual replication of physical systems through real-time data exchange — is rapidly transitioning from industrial engineering to biomedical research. By coupling physics-based computational models with machine learning and continuous sensor streams, DTs create patient-specific, updateable in silico counterparts capable of predicting disease trajectories and optimising therapeutic strategies.
Objective: This review characterises the current landscape of DT applications across biomedical research domains, evaluates emerging use cases, and systematically appraises the technical, ethical, and regulatory challenges that must be resolved before clinical translation at scale.
Methods: A comprehensive narrative review of peer-reviewed literature was performed, integrating sources from biomedical engineering, computational medicine, digital health, and regulatory science published up to 2023.
Results: Digital twins demonstrate clinical utility across cardiac surgery planning, personalised oncology, drug discovery, pandemic modelling, and wearable health monitoring. Key barriers include data heterogeneity, computational demands, regulatory uncertainty, and privacy vulnerabilities.
Conclusion: Digital twin technology holds transformative potential for precision medicine. Progress requires coordinated advances in interoperability standards, federated learning, prospective validation frameworks, and DT-specific regulatory pathways.