A vision for leveraging digital twins to support the provision of personalised cancer care.

Abstract

Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalised care. A recognised aspect in cancer care is the need for more personalised treatment planning to complement the recent advances in precision medicine. In this article, we present a classification of digital twins into Grey Box, Surrogate and Black Box models using systems and mathematical modelling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalised uterine cancer care. This paper presents one of the first attempts to use digital twins in this capacity and represents an amalgamation of three key domains: clinical, digital health and computer science respectively.