PharmaVoice Trendline on Artificial intelligence & machine learning
Following the departure of former FDA Commissioner Marty Makary, the agency-level AI strategy he championed faces uncertainty. Although Acting Commissioner Kyle Diamantas stated that AI remains a priority, attrition in key positions such as the Chief AI Officer may cause the FDA's internal AI applications to revert to a fragmented model. Industry expert Tala Fakhouri believes that regulatory policy toward sponsors' use of AI will not change in the short term, but balancing transparency and rulemaking processes will be a future challenge.
The FDA was all in on AI. Will that change?
High-level turnover has introduced uncertainty about how quickly the FDA’s agency-wide AI strategy will move forward.
Artificial intelligence is here to stay at the FDA, but its integration and evolution may not be a straight line now that one of its biggest proponents, former commissioner Dr. Marty Makary, has stepped down.
Makary was pushing for a more centralized approach to AI, rolling out an agency-wide tool called Elsa, a large language model that could accelerate reviews and evaluations by taking on support tasks such as writing and summarizing reports. The agency also announced plans in December to expand the use of agentic AI to support premarket reviews, inspections and administrative tasks.
Between 2024 and 2025, the number of AI use-cases reported by the FDA skyrocketed by 148% amid a broader government push to leverage the tech, according to a Bipartisan Policy Center study.
Acting commissioner Kyle Diamantas said AI remains a top priority for the FDA. Still, recent high-level departures, including Makary, Jeremy Walsh, chief artificial intelligence officer, and Sridhar Mantha, acting chief information officer, have raised questions about the future of AI implementation.
“It's unclear now what the leadership and governance structure are around FDA-wide efforts,” said Tala Fakhouri, chief artificial intelligence and regulatory strategy officer at Parexel, and a former FDA AI policy official.
FDA officials have spoken about AI at recent conferences, but they were all representing individual divisions, Fakhouri said, noting that this could portend a return to the more fragmented, department-specific AI approach that existed prior. At the same time, efforts to increase transparency around AI use and enact AI-related policy-making could also slow, she said.
These potential setbacks primarily concern the agency’s internal use of AI, and not those governing how drug companies apply the technology in their own work. So far, there hasn’t been a change in the FDA's policies as they relate to the sponsor use of AI.
“And I don't expect to see a change there, which is good,” Fakhouri said.
Evolving use of AI
FDA’s internal use of AI to streamline staff workloads has evolved steadily in recent years. Elsa had its origins in a CDER-developed program, CDER GPT, said Fakhouri. The agency expanded on that original program to use a retrieval-augmented generation system to reduce AI hallucinations. The system is designed to confine the large language model to a well-defined database of trusted information, tailored for the individual centers within the agency, Fakhouri said.
This allows staff to use the tool to access information needed for their unique roles. For example, staff members can quickly generate a summary of industry comments on a particular proposal, or the Office of New Drugs could turn to Elsa to quickly generate a history of regulatory submissions, which can span years.
While Elsa has been leveraged for these types of burdensome tasks, Fakhouri doesn’t believe the technology is used for final decision-making.
Still, “staff can use the tools to augment the work that they're doing. We should all be happy about that,” she said.
Where the FDA’s AI program should go from here
Transparency about how the FDA is using AI in its processes is still lacking, Fakhouri said.
“If the regulators are using AI in certain ways to augment reviewer work or to become an assistant to a reviewer, I think it's good practice for industry to know what these uses look like," Fakhouri said.
Detailing how the FDA is using AI could also foster collaboration with the industry.
“If I am a sponsor or a CRO preparing a submission on behalf of a sponsor, I can prepare my submission with all the data labels and information that might be helpful for the reviewer and the [AI assistant] to be able to review my package,” she said.
Fakhouri said she expects the agency to move toward greater transparency over time.
“But it would require someone in a leadership position at the agency to become aware of that and want to actually make it happen,” she added.
Fakhouri would also like to see the agency streamline AI-related rulemaking that impacts the industry, such as how AI tools are validated for clinical trials.
Under Makary, policy changes were sometimes announced outside the traditional FDA guidance process and through journal articles or press conferences instead. But the agency appears to be returning to its former norms under Diamantas, who recently confirmed that informal statements made by the former commissioner don’t represent official policy.
“They will go through the regular guidance and policy development processes,” Fakhouri said.
While traditional rulemaking insulates pharma from uncertainty, it can also be a slower process that creates its own challenges as companies rapidly adopt different AI platforms
“The fastest guidance that you could put out would still probably take a year. A year in the age of AI is very slow,” she said.
Finding a balance between structure and flexibility will be one of the FDA’s key policy challenges.
“I don't necessarily know what the solution is, but more agile, flexible policy development would be needed, and perhaps more frequent communication between industry and the regulators,” she said.