Cardiovascular health care providers face challenges to obtain accurate diagnosis and optimize treatment for individual patients. General treatment guidelines do not necessarily apply to an individual patient. Consequently, it is difficult to obtain an accurate diagnosis and choose the right treatment for the right patient. The vision of precision medicine may be enabled by the increasing availability of data and the increasing power of computers and algorithms to build an accurate virtual representation of a patient. A hybrid approach of mechanistic models of organs and disease, in combination with statistical reasoning and other artificial intelligence techniques, may especially hold potential to find application to a clinical setting. Boosted by academic exploration, this concept of Digital Twins is increasingly adopted by industry and the health care system, with the cardiovascular field taking a leading position. In this presentation, I will share an industry’s perspective on the potential value of the Digital Twin approach. It will highlight the challenges lying between a purely academic approach and actual application in a clinical setting at large scale. Such challenges include the need for high quality standardized input data, robustness of algorithms, minimal workflow disruption in a clinical setting, extensive clinical validation, regulatory requirements, reimbursement by health care insurers, and the presence of appropriate market channels. A subset of these challenges can be tackled by collaborative efforts from industry and academia. The Digital Twin, bringing complex algorithms to actual clinical application, could provide an ideal breeding ground for such collaborations with maximum clinical impact.