Digital Twins in Modern Healthcare: A Comprehensive Review of Architectures, Applications, and Challenges

Recent developments have witnessed a notable surge in digital healthcare services, encompassing personalized healthcare, intelligent rehabilitation, telemedicine, and smart diet management, among others. These services cater to a diverse array of needs, necessitating proactive intelligent analytics and network self‐sustainability. Network self‐sustainability entails minimal input from end‐users or network operators, while proactive intelligent analytics facilitate efficient resource management in response to user demands. Digital Twins (DTs) emerge as a pivotal solution to enable healthcare with proactive online analytics and self‐sustainability. In this discourse, we provide an overview of recent advancements in DTs for healthcare, elucidating their architecture and proposing a framework tailored to healthcare contexts. Additionally, we showcase various cases of DTs being used in healthcare settings. Finally, we explore open research challenges within the domain, offering potential solutions to address them. This article is categorized under: Applications of Computational Statistics > Health and Medical Data/Informatics Applications of Computational Statistics > Clinical Trials