Medical digital twins combine diverse health data streams and disease modelling to produce a dynamic copy of a patient that guides the clinical team towards personalised treatment while alleviating workload.
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 (physical object)
- Data connection (multimodal health data integration)
- Patient-in-silico (AI- and mechanistic-model-driven virtual copy)
- Interface (for clinician and patient interaction via AI-powered tools like LLMs)
- Twin synchronisation (continuous updating).
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.
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