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Mulder ST, Omidvari AH, Rueten-Budde A, Huang PH, Kim KH, Bais B, Rousian M, Hai R, Akgun C, van Lennep JR, Willemsen S, Rijnbeek PR, Tax DM, Reinders M, Boersma E, Rizopoulos D, Visch V, Steegers-Theunissen R
Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The Dynamic Digital Twin: diagnosis, treatment, prediction and prevention of disease during the life course
A Digital Twin (DT), which is defined originally as a virtual representation of a physical asset,
system or process, is a new concept in healthcare. DT in healthcare cannot be a single technology,
but a domain adapted multi-modal modelling approach, which incorporates the acquisition,
management, analysis, prediction, and interpretation of the data, aiming to improve medical decision
making. However, there are many challenges and barriers that has to be overcome before a DT can
be used in healthcare. In this viewpoint paper, we address these challenges, and envision a dynamic
DT in healthcare for optimizing individual patient health care journeys. We describe how we can
commit multiple domains to developing this DT. With our cross-domain definition of the DT, we aim
to define future goals, trade-offs, and methods, which guide the development of the dynamic DT and
the implementation strategies in healthcare.
Citation
Please cite as:
Mulder ST, Omidvari AH, Rueten-Budde A, Huang PH, Kim KH, Bais B, Rousian M, Hai R, Akgun C, van Lennep JR, Willemsen S, Rijnbeek PR, Tax DM, Reinders M, Boersma E, Rizopoulos D, Visch V, Steegers-Theunissen R
Dynamic Digital Twin: Diagnosis, Treatment, Prediction, and Prevention of Disease During the Life Course