Human Digital Twins for Pervasive Healthcare: A Scoping Review (Preprint)

Abstract

Human digital twins replicate humans in virtual worlds with real-time sensing and machine learning, enabling various services such as visualization, simulation, and prediction. The concept of human digital twins has recently been applied in diverse domains such as manufacturing and healthcare. However, there is a lack of systematic reviews and discussions on the key components of human digital twins and their applications in mental healthcare contexts. This article first offers a scoping review of the purposes of human digital twins and their models and thereafter charts how human digital twins with sensing, mapping, and acting capabilities can serve as an enabling technology for a data-driven, patient-centric, systems approach to pervasive mental healthcare. This article used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for selecting 24 papers from the last 10 years in PubMed, Web of Science, Scopus, and Google Scholar.