LDTH: 1st edition of the Invited Session on

 Leveraging Digital Twins in Healthcare


in conjunction with the

 28th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems


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.


Topics


The Session covers but is not limited to the following topics:

 

 Chairs

Laura Verde, Dr, Department of Mathematics and Physics, Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy.

Jan Vrba, Dr, Department of Mathematics, Informatics and Cybernetics, University of Chemistry and Technology in Prague, Prague, Czech Republic.

Roberta De Fazio, Dr, Department of Mathematics and Physics, Università degli Studi della Campania “Luigi Vanvitelli”, Caserta, Italy.