Digital twin in healthcare: Classification and typology of models based on hierarchy, application, and maturity

The digital twin (DT) is a powerful technological tool that has captured many industries’ attention in recent years, including healthcare where it offers great potential for service quality and operational efficiency. However, the literature in this field remains scattered among heterogeneous applications ranging from the digital twinning of a heart to that of a city’s population health. Although recent reviews may have provided better structure for literature understanding, a typology of healthcare DTs as well as evaluation and implementation guidelines are still missing. Therefore, this article provides a structured review of literature as well as a three-tiered taxonomy and evaluation system to better assess the current state of research on DTs in healthcare and facilitate comparisons among models sharing similar core characteristics. First, we provide a comprehensive review of case studies and use case frameworks in literature and industry based on their hierarchical level of reality, application purpose, and maturity or sophistication of models. Second, we provide an analysis of the maturity and sophistication of models by application type to highlight particular characteristics and facilitate discussion of future opportunities and improvement paths. The proposed classification of reviewed articles provides a better overview of the most studied types of models in research and facilitates the understanding of their potential use in healthcare settings, but is also extendable to other fields as well, as it aims to regroup models in a way that is coherent with existing models in industry.