## 4 DT Use Cases and Application in Healthcare  
Currently, digital twin (DT) technology is primarily utilized in engineering and manufacturing to create precise virtual models of objects and to simulate operational processes. Various DT applications have been explored in operations and supply chain management, including operational traceability, transport maintenance, remote assistance, asset visualization, and design customization, as documented in recent studies [33; 34; 35; 36; 37; 38]. The technology is expected to realize its potential in numerous other sectors, such as automotive, aerospace, construction, agriculture, mining, utilities, retail, healthcare, military, natural resources, and public safety [39; 40; 41; 13; 27; 36; 37; 36; 42; 43]. DTs have achieved notable success across various industries and are now gaining prominence in healthcare. The COVID-19 pandemic acted as a catalyst for the healthcare and life sciences industry, accelerating its digital transformation initiatives [44]. During the pandemic, challenges such as disruptions in the supply chain and limitations in facility capacities necessitated the rapid adoption of DT. These digital twins enable the monitoring, simulation, and streamlining of data from diverse devices. This crisis highlighted the need for more efficient healthcare processes to cope with the massive shifts caused by the pandemic. Furthermore, healthcare providers are facing increasing pressure to digitally transform and meet growing patient expectations. Consequently, there is a rising interest in leveraging technologies like DT within the life sciences sector to enhance operational efficiency and patient care [17]. Data from sources like Electronic Health Records (EHRs), disease registries, and other systems can be integrated with information from wearables and personal health devices. By combining these extensive datasets, we can create precise profiles for individuals. This allows for simulations to forecast the impact of different medications, the development of tailored care plans, and early detection of an individual's vulnerability to illnesses or other conditions. The various associated data and services and concerns of human DTs are represented in Figure 2. DT solutions connect massive digital data networks across their physical assets and products to create living models of facilities, supply chains, and medical products--even body parts and organs [45]. By combining these extensive datasets, we can create precise profiles for individuals. This allows for simulations to forecast the impact of different medications, the development of tailored care plans, and early detection of an individual's vulnerability to illnesses or other conditions. The digital model will allow healthcare providers to combine data and intelligence, ask and answer big-picture questions critical to their survival, and reimagine how they operate, collaborate, and innovate. Additionally, digital twin simulations can also help in the planning stages by providing a platform for interdisciplinary teams of healthcare professionals to collaborate. Surgeons, anesthesiologists, and nurses can run through the simulated procedure, offering their expertise on how to optimize each step. This fosters a more holistic approach to patient care, ensuring that every medical decision is rigorously evaluated from multiple perspectives before being carried out [46]. Different forms of digital twins exist in current practice, but they all fundamentally comprise three main components: the tangible object in the physical realm, its digital representation in a virtual space, and the bi-directional flow of data and information between the two. A digital thread functions as the chronological data pipeline for a digital twin, allowing for continuous tracking and tracing over a specific time span. In healthcare, this could be from the inception to the end of a lifecycle for an individual, a cell, or even a designated time period for a particular population.