Actionable Decision-Making for Digital Twins in Health and Life Sciences

I will assess the status of digital twin technology in the healthcare and life sciences sectors with a focus on how reliable such in silico models are today. To make digital twins useful in practice, they must not only be of high fidelity but capable of providing actionable predictions. The former implies that we can trust their output; the latter that they produce it fast enough to influence decision-making in the relevant context. I will look at two cases — one in cardiovascular medicine, the other in drug discovery - that provide a state-of-the-art assessment of where things stand.