医疗保健
领域(数学)
透视图(图形)
功能(生物学)
具身认知
数据科学
对象(语法)
人机交互
计算机科学
人工智能
数学
进化生物学
纯数学
经济
生物
经济增长
作者
Kang Zhang,Hong-Yu Zhou,Daniel T. Baptista‐Hon,Yuanxu Gao,Xiaohong Liu,Eric K. Oermann,Sheng Xu,Shengwei Jin,Jian Zhang,Zhuo Sun,Yun Yin,Ronald M. Razmi,Alexandre Loupy,Stephan Beck,Jia Qu,Joseph Wu
出处
期刊:Patterns
[Elsevier]
日期:2024-08-01
卷期号:5 (8): 101028-101028
标识
DOI:10.1016/j.patter.2024.101028
摘要
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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