From Virtual Patients to Real Solutions: Medical Innovation on Tap

Cardiac surgery is a high-risk procedure performed to either extend or improve quality of life. Although it has got progressivley safer due to advances in surgical techniques, anaesthesia and intensive care, the risk of serious complications remains.
Can these complications be predicted? If they could be predicted what might be the role of big data, artificial intelligence and computer simulation and could these tools help avoid complications all together?

There are over 500,000 unique medical devices on the market ranging from syringes to MRI scanners, infusion pumps, pacemakers, hospital beds and wound dressings. Before medical devices can be used in the healthcare professionals in the NHS or patients/users, the manufacturer has to demonstrate that the product is safe and performs as intended. In this session, we will explain how manufacturers generate evidence to support their product, different types of evidence and the process that regulators use to check that manufacturers conform to the regulatory requirements.

What if doctors could test medical devices and treatments on a virtual version of you before trying them in real life? In this talk, we'll explore how computer modelling and simulation are becoming a powerful "fourth pillar" of medical evidence, alongside lab tests, animal studies, and clinical trials. I’ll share how I used a virtual population to test brain aneurysm treatments, offering insights without risk to real patients. Join to discover how digital twins and in-silico trials are shaping the future of safer, smarter healthcare.

As medical devices become more widely used, they often venture beyond their originally tested applications into “uncharted territory.” One such case is the treatment of brain aneurysms in the posterior communicating artery. This vessel accounts for a quarter of all aneurysms, yet its unique physiology can make treatment challenging. In this talk, I’ll explore how computational models can assess the effectiveness of off-label treatments, helping to identify when they may work and when they might pose risks, ultimately guiding safer, more informed surgical decisions.