Zhang Kang, Zhou Hong-Yu, Baptista-Hon Daniel T, Gao Yuanxu, Liu Xiaohong, Oermann Eric, Xu Sheng, Jin Shengwei, Zhang Jian, Sun Zhuo, Yin Yun, Razmi Ronald M, Loupy Alexandre, Beck Stephan, Qu Jia, Wu Joseph
National Clinical Eye Research Center, Eye Hospital, Wenzhou Medical University, Wenzhou 325000, China.
Institute for Clinical Data Science, Wenzhou Medical University, Wenzhou 325000, China.
Patterns (N Y). 2024 Aug 9;5(8):101028. doi: 10.1016/j.patter.2024.101028.
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.
数字孪生(DT)是工业领域广泛使用的一个概念,用于创建物理对象或系统的数字副本。物理实体与其数字对应物之间的动态双向链接能够实时更新数字实体。它可以预测与物理对象功能相关的扰动。数字孪生在医疗保健和医学中的显著应用具有极具吸引力的前景,有可能彻底改变患者的诊断和治疗方式。然而,包括技术障碍、生物异质性和伦理考量在内的挑战使得难以实现预期目标。多模态深度学习方法、具身人工智能代理和元宇宙的进展可能会缓解一些困难。在此,我们讨论数字孪生的基本概念、在医学中实施数字孪生的要求及其当前和潜在的医疗保健用途。我们还就医疗保健数字孪生系统推进该领域研究的五个标志提出了我们的观点。