Concepts and applications of digital twins in healthcare and medicine

Kang Zhang(Macau University of Science and Technology), Hong-Yu Zhou(Harvard University), Daniel T. Baptista‐Hon(Macau University of Science and Technology), Yuanxu Gao(Peking University), Xiaohong Liu(University College London), Eric K. Oermann(NYU Langone Health), Sheng Xu(University of California San Diego), Shengwei Jin(Wenzhou Medical University), Jian Zhang(Wenzhou Medical University), Zhuo Sun(Wenzhou Medical University), Yun Yin(City University of Macau), Ronald M. Razmi, Alexandre Loupy(Inserm), Stephan Beck(University College London), Jia Qu(Wenzhou Medical University), Joseph Wu(Cardiovascular Institute of the South)
Patterns
August 1, 2024
Cited by 100Open Access
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Abstract

The digital twin (DT) is a concept widely used in industry to create digital replicas of physical objects or systems. The dynamic, bi-directional link between the physical entity and its digital counterpart enables a real-time update of the digital entity. It can predict perturbations related to the physical object's function. The obvious applications of DTs in healthcare and medicine are extremely attractive prospects that have the potential to revolutionize patient diagnosis and treatment. However, challenges including technical obstacles, biological heterogeneity, and ethical considerations make it difficult to achieve the desired goal. Advances in multi-modal deep learning methods, embodied AI agents, and the metaverse may mitigate some difficulties. Here, we discuss the basic concepts underlying DTs, the requirements for implementing DTs in medicine, and their current and potential healthcare uses. We also provide our perspective on five hallmarks for a healthcare DT system to advance research in this field.


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