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Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalized care. A recognized aspect in cancer care is the need for more personalized 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 modeling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalized uterine cancer care. This article 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.
Wickramasinghe et al. (Thu,) studied this question.