By Jon Walsh, Chief Scientific Officer

Last month, I had the privilege of speaking at the FDA-CTTI Workshop on Artificial Intelligence in Drug & Biological Product Development. I joined Session 4, "Navigating the Future of AI in Drug Development," alongside fellow panelists Dr. Jessilyn Dunn from Duke University and Ryan Hoshi from AbbVie, in a session moderated by Rebecca Nebel (PhRMA) and Gabriel Innes (FDA).

My talk, "Using AI for the Hard Problems," focused on a necessary transition we need to make as an industry: the shift from human-centric to model-centric development.

For decades, drug development has been a biological, chemistry-driven field. It has also been deeply human-centric. We focused on training people, defining best practices for them to follow, and building complex systems. We wanted to understand every decision, often slowly and in ways that act against efficiency gains from software and AI.

But as I argued during my session, if we want to solve the hard problems—like finding safe, effective drugs for complex diseases or predicting climate change—we have to let the models do what they are good at.  And we have to set up the business processes that allow that to happen.

In physics, we model the Large Hadron Collider—a $1 billion-a-year experiment—to understand fundamental truths about the universe. Clinical trials are a $40 billion-a-year experiment on people. The scale is massive, and relying solely on human-centric systems limits us. We need to move toward a model-centric approach where:

• Best practices ensure quality. We circumvent the "black box" fear by having rigorous rules for how models are developed and used.
• Performance rules. The attitude becomes "just use a model, it's going to do a better job," and interpretability becomes less relevant than accuracy.
• We use systems–cleaning, harmonizing, and using datasets; using software and AI–that make it easier to build and use models and integrate them into our decision-making processes.

But you can't just flip a switch to model-centricity. You need proof points to overcome the high mistrust and lack of clear value demonstration that often plagues new tech.

I highlighted a specific use case we are championing at Unlearn: using AI models to predict participant outcomes in a clinical trial. By integrating these predictions into the analysis—creating what we call model-derived covariates—we can safely add statistical power and eventually reduce sample sizes. We hope using these tools becomes a de facto expectation because it is simply the better, safer way to run a trial.

One of the most encouraging moments from the discussion came during the closing remarks from Qi Liu, the Associate Director for Innovation & Partnership at the FDA’s CDER.

We often view regulators as the brakes on innovation, but in this workshop, they were helping steer the car. Qi Liu shared a perspective on digital twins that effectively validated that the agency is looking at these technologies not just as novelties, but as legitimate tools for modernizing clinical trials. Her comments underscored a critical shift: the FDA is open to advanced methodologies—provided we have the rigorous frameworks to assess them.

We left the workshop with a clear to-do list for both sides of the aisle:

• For Regulators: Continue defining the framework. As I mentioned in my talk, regulators are actually out in the lead in some ways. We need them to sharpen the boundaries for not yet versus work with us to evaluate and help define case studies that the whole community can use.
• For Sponsors: We need to acknowledge that drug development is fundamentally a computational science now. That means investing in data scientists and machine learning engineers—giving them a seat at the table, where they currently often don't have one.

The transition to a model-centric future is inevitable. The question is no longer if AI will transform drug development, but how quickly we are willing to unlearn our old habits to make it happen.