The complexity of our healthcare systems, the fascinating and intricate mechanisms of human pathophysiology, the societal, ethical, and regulatory barriers in the adoption of new technologies, and the intrinsic difficulty in the development of trustworthy solutions, are some of the challenges which we tackle through this CDT.
Digital Twin technologies provide a pathway to more efficient, data-driven, personalised, and preventive healthcare. A Digital Twin is a computational replica of a physical entity, continuously updated through time, to inform decisions. In healthcare, we can twin
- individual patient’s physiology, such as the cardiovascular system;
- an asset, for example a robotics entity or drug manufacturing facility;
- or a healthcare organization, such as the oncology department in a hospital.
Key to Digital Twin technologies is the unprecedented computational ability to support human decision making, with both inductive reasoning from data, using machine learning techniques, and deductive mechanistic modelling. The continuously optimized pairing of induction and deductions, aimed to be used in decision making, forms a Digital Twin.