In May and June of 2025, the Food and Drug Administration (FDA) announced a series of important developments related to the use of artificial intelligence (AI) across its processes. These developments signaled a significant move towards integrating technological advancements into the FDA’s routine tasks to enhance productivity.
Notably, these enhancements are likely to accelerate scientific and clinical processes, particularly in the context of drug development and regulatory approval. In this blog, we cover the motivations behind the initiatives, what they will entail, and how they could potentially impact the field of precision medicine.
Why now?
The FDA’s recent adoption of AI comes at a critical juncture for medical innovation. Scientific momentum has been building significantly over recent years, with breakthroughs in gene therapy, cell-based treatments, and RNA technologies. These have transformed outcomes among patients with rare and previously untreatable conditions.
Technological innovations must be matched by agility on the regulatory side. Since many of these new therapies are developed for small populations with high unmet need, time is of the essence. If the science exists to develop these treatments quickly, the regulatory framework must evolve to evaluate them just as rapidly.
At the same time, AI technologies have matured to the point where they can augment and automate complex regulatory tasks. Advances in large language models, knowledge retrieval, and document synthesis now allow agencies to reimagine how safety, efficacy, and compliance are assessed across the drug development lifecycle. In this context, the question is no longer if AI belongs in regulatory science, but how to deploy it responsibly to meet today’s unprecedented pace of therapeutic discovery.
FDA uses AI to assist in scientific review for the first time
The FDA’s scientific review process is a tedious and laborious process - justifiably so, as it is a crucial part of drug evaluations. However, many of the required tasks are repetitive and amenable to automation. This motivated the decision to test the performance of generative AI in assisting experts to complete scientific reviews.
The pilot was reported to be a major success and saved experts a lot of time, with certain tasks in the scientific review process being reduced from three days to a few minutes. FDA leadership highlighted that this would reduce non-productive tasks that were previously part of the process, therefore freeing up time for tasks requiring deep expertise and recognizing the value of their scientists’ time.
FDA releases Elsa and announces wider plans for AI
Alongside the announcement of the pilot program, the FDA aimed to scale up AI integration across the agency by the end of June. This included expanding use to other processes, improving performance, and adapting functionality based on the needs of each center.
Soon after, the FDA announced the launch of their generative AI tool, Elsa, and provided additional details on its applications. In addition to scientific review, Elsa is assisting FDA employees with reviewing clinical protocols and identifying inspection targets. Importantly, the large language model was not trained on sensitive research data.
Talks between the FDA and OpenAI to streamline drug approval
The FDA is also interested in using AI tools to help accelerate drug approval processes. In line with this, they have entered early-stage discussions with OpenAI, the organization behind ChatGPT, to explore how generative AI tools might streamline the drug approval process. These conversations have focused on identifying areas where large language models can assist with high-volume, time-intensive regulatory tasks.
The potential impact is significant, since the FDA’s Center for Drug Evaluation and Research (CDER) handles tens of thousands of submissions each year, ranging from Investigational New Drug applications to post-market safety updates. The project is therefore called cderGPT.
Even modest improvements in efficiency could translate into hundreds of thousands of review hours saved annually (and a faster evaluation process). While still in an exploratory phase, cderGPT could support reviewers in document classification, summarizing trial protocols, flagging missing data, or mapping applications against regulatory standards. It is important to mention that these processes would be overseen by FDA employees.
Implications for clinical research and precision medicine
The speedup of scientific reviews and clinical protocol assessments could dramatically reduce time-to-market for targeted therapies. This is particularly relevant for precision medicine companies, whose submissions often involve complex data, adaptive trial designs, and real-world evidence.
If tools like Elsa prove reliable, developers could see fewer bottlenecks during regulatory interactions. Nevertheless, it raises questions about how AI-generated outputs will be documented, audited, and trusted in regulatory settings, particularly since reproducibility and explainability are essential.
Piloting internal AI assistants for study design at Sano Genetics
As regulatory bodies move toward greater AI adoption, industry innovators are following suit. At Sano Genetics, engineering teams have already begun piloting internal AI assistant prototypes designed to accelerate clinical study design. These tools aim to reduce time spent drafting protocols from scratch, compiling eligibility questionnaires, and setting up studies.
In addition to speeding up initial design, the tools are also being developed to support the rapid scaling of existing studies. This can facilitate adapting studies to new regions, populations, or disease areas with minimal manual effort. While still in early stages, these pilots are moving us closer to making the process of trial planning and design faster, more flexible, and better aligned with the needs of precision medicine.
William Jones, Chief Technology Officer at Sano Genetics, emphasized the importance of thoughtful integration: “Sano’s philosophy has been to internally define what works best for our industry and goals. Now, we’re focused on weaving that work into our core product, first for administrators and eventually for participants, under clearly defined use cases.”
What’s next?
The FDA’s integration of AI tools like Elsa and its exploration of broader collaborations signals a shift from experimentation to application. As these tools scale across regulatory workflows, they are expected to not only increase efficiency but also reshape regulatory processes.
This shift could pave the way for more adaptive, data-rich regulatory processes that align with the speed and complexity of modern drug development. However, this isn’t about replacing human expertise, but rather augmenting it by freeing scientists and reviewers to focus on judgment-based decisions while AI handles the menial, repetitive tasks.
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