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Elsa: FDA’s New AI Tool To Streamline Drug Review Process
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Last Updated: Jun 4, 2025
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Fact-Checked by Sarah Frazier Content Manager
The U.S. Food and Drug istration (FDA) announced Elsa as their new generative AI tool to accelerate internal work, such as scientific reviews, data management, and protocol assessments. The tool was rolled out ahead of schedule and under budget.
In a rush? Here are the quick facts:
- Elsa helps FDA staff summarize documents and compare drug safety labels.
- The tool was rolled out ahead of schedule and under budget.
- Experts raised concerns about security and real-world integration of AI.
FDA Commissioner Marty Makary stated that the Elsa tool was released ahead of schedule and under budget because of the collaboration between in-house experts across the centers.
Elsa is a large language model–powered tool designed to help FDA staff read, write, and summarize. It can highlight adverse events in drug reports, compare labels quickly, and even generate code for building nonclinical databases.
This move comes as part of the FDA’s broader plan to bring AI into everyday operations. Elsa was first tested with scientific reviewers and will now be available across the agency in a secure cloud environment. Officials say the system does not learn from data submitted by drug companies, which helps keep sensitive information safe.
FDA Chief AI Officer Jeremy Walsh declared that the release of Elsa marks the beginning of the AI era at the FDA. “AI is no longer a distant promise but a dynamic force enhancing and optimizing the performance and potential of every employee.”
Elsa is already in use for reviewing clinical trial protocols faster and identifying high-priority inspection sites, as noted by Reuters.
However, concerns have been raised, as noted by Axios. Some public health experts question how secure the tool really is and whether it can be integrated quickly into daily workflows. According to Stat, some FDA employees also expressed unease, saying the pilot mainly focused on text summarization and may not reflect broader performance.
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