The Governance Era Arrives for AI in Clinical Trials
The biotech industry, never short on acronyms or ambition, has been flirting with artificial intelligence for years.
Models have already been shaping protocol designs, accelerated site selection, and sifted through trial data faster than any human team could hope to match. But until recently, these systems operated in a kind of liminal zone.
AI's been used widely, spoken about vaguely, but governed mostly by internal risk assessments and the slow drip of evolving FDA guidance.
That began to change last week, when Advarra, one of the most influential infrastructure providers in the clinical trial space, announced the formation of the Council for the Responsible Use of Artificial Intelligence in Clinical Research.
It’s a long name for what may become a defining moment in how AI is evaluated, measured, and ethically governed across drug development.
The council launches with real institutional weigh with founding members including Sanofi, Recursion, and Velocity Clinical Research, along with representatives from CROs, data standards groups, and tech companies. In other words, they're launching with the people responsible for accelerating enrollment, optimizing protocols, and navigating the ever-narrowing corridor between speed and safety in global trials.
The purpose of the council is simple but overdue. Clinical research has embraced AI, but without shared frameworks, adoption has been uneven and often opaque. Most systems are built in silos, benchmarked inconsistently, and rarely interrogated for bias or unintended effects on trial outcomes.
As AI tools touch more of the patient journey, from initial eligibility assessments to real-time monitoring, the absence of standards has become more than inconvenient, it’s a liability.
The Council aims to create common governance frameworks, metrics, and model oversight practices for AI systems used in clinical research. That includes everything from feasibility and recruitment to digital biomarkers and protocol adherence. The ambition, according to Advarra, is to “operationalize trustworthy AI,” which may be the most quietly radical phrase in the entire announcement.
Industry voices have been saying this for some time. Michel Rider, who leads clinical data and digital operations at Sanofi, pointed out that without real-world benchmarks and governance models, even the most promising tools risk stalling before they scale.
Sid Jain of Recursion echoed the sentiment, noting that many organizations have yet to fully realize AI’s potential because their internal systems aren't optimized for learning from study data. He pointed to enrollment trends, protocol amendments, and investigator behavior as underused signals that, when properly modeled, could radically reshape trial design.
But until now, those ideas have stayed mostly within company walls or fragmented working groups. What Advarra is trying to do is different. The Council will convene regular workgroups, publish peer-reviewed research, and push for shared standards that can be adopted across sponsors, CROs, and sites.
It is, as Advarra’s CTO Bryan O’Byrne put it, not a thought exercise. It’s meant to be applied, structured, and built for public accountability.
Why now? The timing is not coincidental.
Earlier this year, the FDA issued draft guidance on the use of AI and machine learning in drug development. It stopped short of strict regulation, but its tone was unmistakable: explainability, bias mitigation, and ongoing monitoring are no longer optional.
Companies are being asked to prove their AI systems do what they claim, and that those systems don’t produce uneven results across populations or geographies. That’s a high bar, especially in an industry known more for proprietary models than open dialogue.
The Council, then, becomes more than a governance body. It is a staging ground for alignment. If successful, it could become the foundation for how regulatory bodies evaluate AI-based tools—not just on technical performance, but on patient impact, ethical boundaries, and long-term safety.
Still, questions remain. Who else will join? How will recommendations be enforced, if at all? What happens when members disagree, especially across competitive lines?
The Council promises transparency, but this is a domain where competitive advantage often trumps collaboration. The measure of success will not be the number of white papers issued, but whether trial sponsors, regulators, and vendors alike begin to speak a shared language about how AI behaves in live clinical environments.
For now, the announcement marks a turning point. AI in clinical trials has matured past novelty and past the first wave of hype. It is now being asked to grow up. That means structure, standards, and above all, a shift in how systems are evaluated—not just by how fast they move, but by how responsibly they are built.
In that sense, Advarra’s new Council is not a footnote in AI’s long march through biomedicine. It’s a declaration that the era of AI governance in clinical trials has begun—and that the people running the world’s most complex experiments are finally demanding answers from the models shaping them.
