The usage of digital twins in healthcare for personalised care

The use of digital twins in healthcare is rapidly increasing, and one application area is the personalisation of medical care, where digital twins can take the shape of in silico models of organs and organ systems used to test various treatment options, to customise therapy or plan surgery.

Also in the development of medical therapies (drugs & devices), digital twins can be used as a tool throughout the entire R&D process to identify knowledge gaps and flaws, obtain a holistic and better understanding of a patient's disease, design novel strategies, optimise therapies, optimise therapy production, increase safety and shorten the time to market.

A wide range of definitions of the concept of digital twins exist, mostly varying in the requirements on the nature of the coupling between the physical entity and its digital counterpart. If, in general, digital twins are thus believed to imply a continuous and real-time interrelation between the digital and physical world, in healthcare, however, a more specific definition is usually employed regarding digital twins of systems in the living world, in the sense that this coupling does not always need to be a real-time one.

Until now, current digital twin solutions for healthcare have been mostly single-scale, single-organ, single disease systems, given the fact that going beyond this appeared to be highly complicated and time-consuming.  More recently, however, ambitious goals have started to surface, aiming at the vision of a validated, integrated multi-scale, -time and -discipline digital twin of the whole body, enabling the comprehensive characterization of the physiological and the pathological state in its full complexity and heterogeneity. This translates into the prospective need of some distributed digital infrastructure, made of data and model standards, standard operative procedures (SOPs), and standardised application programming interfaces, that will allow researchers and developers of digital twins to accrue not only predictive models, but possibly also the experimental and clinical data employed in building and validating these models, into some shared facility for reuse and collaboration.

Following-up on previous sessions focusing on health data sharing issues and privacy-enhancing technologies, the HDIG proposes to dedicate its meeting in Seoul at P19 to exploring the usage and the outlook of digital twins in healthcare, ensuring inclusiveness, fairness, and transparency.