Abstract
Digital Twin (DT) technology has experienced substantial advancements and extensive adoption across various industries, aiming to enhance operational efficiency and effectiveness. Defined as virtual replicas of physical objects, systems, or processes, Digital Twins enable real-time simulation, monitoring, and analysis of real-world behavior. This comprehensive review delves into the evolution of DT technology, tracing its journey from conceptual origins to contemporary technological implementations. The review provides detailed definitions, a classification of different types of Digital Twins, and a comparative analysis of their architectures. Furthermore, it investigates the application of DT technology in diverse sectors, with a particular emphasis on medicine and manufacturing, exemplified by use cases such as personalized medicine. Moreover, the review highlights emerging trends and future directions in DT technology, underscoring the transformative potential of integrating artificial intelligence and machine learning to augment DT capabilities. This analysis not only elucidates the current state of DT technology but also anticipates its future trajectory and impact across multiple domains.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
