Aims and scope

Digital Twins (DTs) are emerging as a revolutionary factor in several industries due to their ability to provide a digital and dynamic representation of a physical system or process. With their ability to provide detailed and dynamic information, DTs enable more effective, efficient and predictive management of physical systems and industrial processes. 

DTs can have multiple functions in healthcare. On the one hand, it is possible to create digital models of patients that contain anatomical, physiological and historical data. These models can be used to tailor treatment plans, simulate complex surgeries and predict individual responses to specific therapies. On the other hand, DTs can be used to monitor the condition and performance of medical devices in real time, predict preventive maintenance and improve the safety and reliability of instruments used in clinical settings. In addition, the flexibility of DTs layered architecture allows the embedding and the combination of several sophisticated analysis techniques –  Machine Learning, Process Mining, Deep Learning, Model-driven approaches – that increase the dependability of the results obtained.