Nadeem Mubaris, Kostic Sascha, Dornhöfer Mareike, Weber Christian, Fathi Madjid
Faculty IV: School of Science and Technology, Institute for Knowledge-Based Systems and Knowledge Management, University of Siegen, Siegen, Germany.
Digit Health. 2025 Jan 7;11:20552076241304078. doi: 10.1177/20552076241304078. eCollection 2025 Jan-Dec.
Digital twins (DTs) emerged in the wake of Industry 4.0 and the creation of cyber-physical systems, motivated by the increased availability and variability of machine and sensor data. DTs are a concept to create a digital representation of a physical entity and imitate its behavior, while feeding real-world data to the digital counterpart, thus allowing enabling digital simulations related to the real-world entity. The availability of new data sources raises the potential for developing structured approaches for prediction and analysis. Similarly, in the field of medicine and digital healthcare, the collection of patient-focused data is rising. Medical DTs, a new concept of structured, exchangeable representations of knowledge, are increasingly used for capturing personal health, targeting specific illnesses, or addressing complex healthcare scenarios in hospitals.
This article surveys the current state-of-the-art in applying DTs in healthcare, and how these twins are generated to support smart, personalized medicine. These concepts are applied to a DT for a simulated health-monitoring scenario.
The DT use case is implemented using AnyLogic multi-agent simulation, monitoring the patient's personal health indicators and their development.
The results indicate both possibilities and challenges and provide important insights for future DT implementations in healthcare. They have the potential to optimize healthcare in various ways, such as providing patient-centered health-monitoring.
数字孪生（DTs）是在工业4.0和网络物理系统创建之后出现的，其动机是机器和传感器数据的可用性和可变性不断增加。数字孪生是一种创建物理实体的数字表示并模仿其行为的概念，同时将现实世界的数据输入到数字对应物中，从而实现与现实世界实体相关的数字模拟。新数据源的可用性提高了开发结构化预测和分析方法的潜力。同样，在医学和数字医疗领域，以患者为中心的数据收集也在增加。医学数字孪生是一种结构化、可交换的知识表示新概念，越来越多地用于捕捉个人健康状况、针对特定疾病或解决医院中复杂的医疗场景。
本文调查了数字孪生在医疗保健领域的应用现状，以及如何生成这些孪生体以支持智能、个性化医疗。这些概念被应用于一个模拟健康监测场景的数字孪生。
数字孪生用例是使用AnyLogic多智能体模拟实现的，用于监测患者的个人健康指标及其发展情况。
结果表明了数字孪生在医疗保健领域应用的可能性和挑战，并为未来数字孪生在医疗保健领域的实施提供了重要见解。它们有可能以各种方式优化医疗保健，例如提供以患者为中心的健康监测。
