review · The Lancet Digital Health
Medical digital twins adapt engineering concepts to healthcare by creating dynamic, continuously updating virtual counterparts of patients for simulation, analysis, and clinical prediction. Five essential components define their architecture: the patient, the data connection, the virtual patient model, the interface, and twin synchronisation. Realising their clinical potential depends on integrating multimodal health data, mechanistic modelling, and artificial intelligence. Combining mechanistic models with artificial intelligence addresses individual shortcomings found when using either method alone. Furthermore, the framework strengthens the performance of large language models within medical settings. Practical examples in fields such as oncology and diabetes illustrate how these connected systems can help resolve complex healthcare challenges, offering guidance for researchers, medical practitioners, and policy makers working towards translating theoretical virtual twins into routine clinical workflows.
Healthcare systems increasingly seek personalised approaches to patient care. Establishing a clear definition and architectural framework for medical digital twins allows clinicians and researchers to better predict treatment responses and simulate disease trajectories. By uniting data fusion, artificial intelligence, and mechanistic models, these virtual representations can transform complex medical decision-making and improve clinical outcomes in major disease areas such as oncology and diabetes.
Applications focus on clinical decision support, simulation, and predictive monitoring for conditions such as oncology and diabetes. Anticipated users include clinicians, medical researchers, and health systems. The technology remains at an early conceptual and foundational stage, with current work addressing structural definitions, data fusion, and technological requirements necessary to bridge the gap between theoretical frameworks and eventual clinical adoption.
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The notion of medical digital twins is gaining popularity both within the scientific community and among the general public; however, much of the recent enthusiasm has occurred in the absence of a consensus on their fundamental make-up. Digital twins originate in the field of engineering, in which a constantly updating virtual copy enables analysis, simulation, and prediction of a real-world object or process. In this Health Policy paper, we evaluate this concept in the context of medicine and outline five key components of the medical digital twin: the patient, data connection, patient-in-silico, interface, and twin synchronisation. We consider how various enabling technologies in multimodal data, artificial intelligence, and mechanistic modelling will pave the way for clinical adoption and provide examples pertaining to oncology and diabetes. We highlight the role of data fusion and the potential of merging artificial intelligence and mechanistic modelling to address the limitations of either the AI or the mechanistic modelling approach used independently. In particular, we highlight how the digital twin concept can support the performance of large language models applied in medicine and its potential to address health-care challenges. We believe that this Health Policy paper will help to guide scientists, clinicians, and policy makers in creating medical digital twins in the future and translating this promising new paradigm from theory into clinical practice.
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DOI: 10.1016/j.landig.2025.02.004
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