Medical digital twins: enabling precision medicine and medical artificial intelligence

Abstract

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.

Figure 1:. Outline of a medical digital twin

The physical object is described by a plethora of different data modalities (eg, electronic health records, imaging studies, and genetic data), which are processed and combined using data fusion approaches forming the data connection. The combined information is forwarded to the patient-in-silico model to visualise the disease, assess disease prognosis, and simulate treatments. The interface, with the aid of artificial intelligence (AI), allows the clinical team and patient to select an optimal treatment plan based on the patient-in-silico. The cycle is repeated as new patient data become available, synchronising the patient and the patient-in-silico (twin synchronisation). Figure created with BioRender.com.

Figure 2:. Enabling technologies

The process of creating a medical digital twin begins with multimodal data. Artificial intelligence (AI) approaches are uniquely positioned to predict and extract parameters from complex data sources, such as tumour size and descriptors from test or genetic risk scores. In addition, AI can incorporate the different parameters to predict disease prognosis and treatment response, requiring only limited understanding of the disease. In contrast, mechanistic modelling can incorporate disease mechanisms and patient-specific parameters to predict disease prognosis and treatment response but is limited to simpler, well understood systems because mechanistic modelling relies on well defined mathematical relationships. The merging of AI and mechanistic modelling allows high-fidelity models, such as the patient-in-silico, that incorporate complex data types with a-priori knowledge about disease mechanics. ODE=ordinary differential equation. PDE=partial differential equation. Figure created with BioRender.com.

Figure 3:. Retrospective data staging

Retrospective data staging allows the construction of medical digital twins using existing data. Data are separated into segments based on treatment regimens, which are then incrementally provided to the medical digital twin for updating and formulation of predictions. Retrospective studies are valuable for patient-in-silico validation and uncertainty quantification. Here, we illustrate a medical digital twin for the prediction of changes in tumour size and mutational load after every treatment step based on retrospective data. Figure created with BioRender.com.