The gap between generating clinical evidence and translating it into practice remains one of the industry’s most stubborn challenges. We have the data – now we need to change decisions. This keynote explores what it takes to move AI-driven tests from validation to adoption – drawing on real-world data, build-versus-buy decisions and the role of successful partnerships in accelerating adoption

The industry is moving faster than the systems built to govern it. This keynote explores the tension between what AI-native drug discovery makes possible and what the regulatory infrastructure can currently absorb, and what needs to change before the gap becomes a crisis.

Clinical development is changing faster than most operating models can keep up with. This panel explores what a genuinely modern ClinOps model looks like, where AI is actually moving the needle in trial design and execution, and where the gap between ambition and reality still needs to close.

From regulatory constraints to scientific opportunity: a practitioner's view on how AI is reshaping what's possible in clinical development, and where the industry needs to push harder.

Clinical trials generate some of the most complex data in medicine and still struggle to get the right patients in the door. Both problems have better tools than ever. Both are still broken. This panel puts two very different parts of the problem in the same conversation — the operational reality of bringing patients to trials at scale, and the scientific challenge of making AI-driven data analysis actually work inside a live programme.

You can build the perfect trial on paper. But perfect trials still fail, because the infrastructure isn't there, the data can't be accessed, the patients aren't reachable, and the corporations moving the pieces are too slow to adapt. This keynote takes a practitioner's view on where AI is genuinely accelerating drug development, and where the hard problems still sit, from fragmented data ecosystems and privacy barriers to the institutional inertia that no algorithm can fix. A candid, sometimes controversial take on what the science is actually showing us, and what the industry needs to stop pretending it has figured out.

Every clinical trial starts with a protocol. Before a single patient is enrolled, that protocol has to survive months of contract negotiations, budget approvals, and legal reviews that haven't meaningfully changed in decades. The industry accepts this as normal. It isn't. This session makes the case that the startup phase is one of the most expensive and damaging bottlenecks in clinical development, puts a real number on what that costs, financially and for patients, and shows what a 70% reduction in startup timelines actually looks like in practice.

AI has been part of drug development longer than most people realise. The question is no longer whether it can help. It's whether the industry knows how to use it properly, who is accountable when it doesn't, and what it actually takes to build at the frontier without breaking what's already working. Founders, investors, and board-level leaders in the same room for the conversation the industry is only beginning to have.

A founder's perspective on what it takes to build AI solutions that actually work in clinical development, what The End. is about, and what comes next.