Digital Twins In Healthcare: Model-based Precision Medicine
A digital twin in healthcare is a virtual representation of a patient’s condition or pathology. A digital twin supports real-time simulation, prediction, and clinical decision-making, and is built for a specific context of use. Twins are predominantly mechanistic, governed by first principles, but increasingly statistical modelling captures data-driven aspects such that hybrid twins emerge. With this convergence, the digital twin concept becomes a comprehensive methodology to realize model-based precision medicine, by integrating simulation technologies as digital medical product. Developing such digital twins is a complex, multidisciplinary challenge that requires technologies at varying levels of maturity to be harmonized and integrated.
The talk by Sven Hirsch, professor at the ZHAW School of Life Sciences and Facility Management, will introduce basic concepts of digital twins and highlight our recent advancement in creating digital twins for hemorrhagic stroke, specifically higlighting machine learning–based medical image analysis and statistical modeling of clinical data through graphical networks, and digital health solutions using wearable data. Digital twin in healthcare is certainly a technological challenge, but above all this new technology poses a socio-cultural challenge involving privacy, identity, and trust.