TY - JOUR
T1 - Concepts and applications of digital twins in healthcare and medicine
AU - Zhang, Kang
AU - Zhou, Hong Yu
AU - Baptista-Hon, Daniel T.
AU - Gao, Yuanxu
AU - Liu, Xiaohong
AU - Oermann, Eric
AU - Xu, Sheng
AU - Jin, Shengwei
AU - Zhang, Jian
AU - Sun, Zhuo
AU - Yin, Yun
AU - Razmi, Ronald M.
AU - Loupy, Alexandre
AU - Beck, Stephan
AU - Qu, Jia
AU - Wu, Joseph
AU - International Consortium of Digital Twins in Medicine
N1 - Publisher Copyright:
© 2024 The Authors
PY - 2024/8/9
Y1 - 2024/8/9
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85207599570&partnerID=8YFLogxK
U2 - 10.1016/j.patter.2024.101028
DO - 10.1016/j.patter.2024.101028
M3 - Review article
AN - SCOPUS:85207599570
SN - 2666-3899
VL - 5
JO - Patterns
JF - Patterns
IS - 8
M1 - 101028
ER -