The U.S. Food and Drug Administration switched on its agency-wide generative AI system Elsa on June 2, 2025. The promise was faster scientific reviews, less bureaucratic “busywork,” and ultimately quicker access to new therapies. It’s only been six months, and the system is not without its flaws, but it’s definitely marked the start of a new AI era in how food, drugs, and devices are regulated. What effects of deployment can we see so far?
What Elsa is – and who is behind it
Elsa (Electronic Language System Assistant) is the FDA’s internal, agency-wide generative AI tool, built to work on the internal document collections, and used by such employees as scientific reviewers and investigators. According to the FDA, Elsa is built within a high-security GovCloud environment that provides a secure platform for employees. Its models were pre-trained by the vendor on broad, generic text data, and do not train on data submitted by the regulated industry.
That reduces some IP and confidentiality concerns, while leaving open the question of how thoroughly its outputs are validated.
An industry analysis from the KAMI Think Tank, citing FDA materials and internal briefings, reports that Elsa is built on Anthropic’s Claude family of models, wrapped in an agency-specific interface and governance layer.
What Elsa can do
Elsa’s reported core functions are relatively narrow but high-impact in terms of staff time:
– Assist with reading, writing, and summarizing internal regulatory documents, including clinical protocol reviews and other scientific evaluations;
– Help staff draft and edit internal documents;
– Summarize adverse events to support safety profile assessments;
– Perform faster comparisons of labels and packaging inserts;
– Generate code to help develop databases for nonclinical applications.
Outputs are advisory and human reviewers remain responsible for regulatory decisions.
Six months in: what do we know about performance?
There is still no public, quantitative evaluation of Elsa’s impact on overall approval timelines or error rates. The evidence that exists is a patchwork of pilot anecdotes, management statements and internal modelling.
– In May 2025, the FDA announced completion of its first “AI-assisted scientific review pilot.” A deputy director in CDER’s Office of Drug Evaluation Sciences, Jinzhong (Jin) Liu called the technology a “game-changer,” saying it allowed him “to perform scientific review tasks in minutes that used to take three days.”
– A KAMI Think Tank review of AI in regulatory affairs, drawing on that pilot, reports that one reviewer saw Elsa complete work in six minutes that previously took two to three days, but again this is a single-case anecdote.
– Commentaries from law firms and consultancies (Hogan Lovells, King & Spalding, Gardner, others) uniformly note potential review time savings but concede that “whether this tool will assist FDA with its workload and result in more timely feedback remains to be seen.”
– In June, Azmed modeled a scenario where trimming 25% of “administrative touch-time” for imaging AI submissions could advance real-world clearance dates by four to six weeks.
Hallucinations and “fake studies”: staff experience from the inside
If efficiency anecdotes are coming top-down, concerns about accuracy are coming bottom-up.
A CNN investigation, widely summarized by Gizmodo and discussed across Reddit, quoted six current and former FDA officials. Three described Elsa as helpful for routine tasks like meeting summaries. Three others reported that the system “spits out fake studies” and misrepresents existing research – classic large-language-model hallucinations. One employee put it bluntly: “Anything that you don’t have time to double-check is unreliable. It hallucinates confidently.”
Additional reporting from Medical Economics and TechSpot describes internal feedback labelling Elsa as “clunky,” prone to hallucinations, and rolled out in a way that prioritized political optics over usability. A RAPS Focus article titled “Elsa gets mixed response from some staff” reports a split pattern: early adopters using it as a better search and summarization tool, and skeptics who avoid it for anything that touches core scientific judgment.
On semi-anonymous forums like r/DeptHHS and regulatory affairs, self-identified FDA staff are even less diplomatic, describing Elsa as “not a very intelligent AI” that “crashes every hour,” and, in one dry comment, as something that helps leadership show they did “five things this week” in line with the administration’s AI agenda.
How the broader industry is reacting
For the industry, Elsa has quickly become both a symbol and a practical consideration.
Law-firm briefings (Morgan Lewis , Hogan Lovells, McDermott, King & Spalding) treat Elsa as a structural reorganization in how dossiers will be read: by humans and by an LLM that is good at spotting missing sections, inconsistencies and misaligned labels. They urge sponsors to assume their submissions will be parsed automatically and to plan for more structured, machine-readable evidence packages.
In academic and policy literature, Elsa is increasingly cited as a case study of regulators turning AI on themselves. A 2025 review of AI and international regulatory frameworks highlights Elsa as emblematic of the shift from regulating AI to using AI in regulatory workflows, while warning that transparency about validation, governance and appeal mechanisms is still limited.
A medRxiv preprint on AI flexibility in the FDA regulation mentions Elsa as an example of internal AI use but focuses on conceptual governance issues rather than performance metrics.
Elsa against the backdrop of FDA layoffs
Any assessment of Elsa’s impact has to sit against the staffing shock that hit HHS and the FDA in 2025.
On March 27, 2025, HHS Secretary Robert F. Kennedy Jr. unveiled a restructuring plan that would cut 10,000 positions across the department, including about 3,500 at the FDA, as part of a broader reduction of roughly 20,000 HHS staff through layoffs and voluntary departures.
In his first Senate budget hearing on May 22, 2025, FDA Commissioner Marty Makary said 1,900 FDA employees had already been laid off and that roughly 1,200 more had accepted early-retirement packages. He emphasized that no scientific reviewers or inspectors were included in the reduction in force and that the staffing changes had not affected the agency’s ability to meet its Prescription Drug User Fee Act (PDUFA) review goals.
Outside analyses are less reassuring. Legal and regulatory-strategy firms describe layoffs hitting inspection support staff and pausing some inspection initiatives, with inspectors now absorbing administrative work and becoming more reliant on remote, paper-based reviews. Practitioners who work with the FDA report emerging frictions in informal communications and smaller administrative interactions with the agency, even where headline review timelines are officially said to be on track.
Elsa therefore arrives as an AI system rolled out quickly into an agency that has just lost several thousand colleagues and whose leadership is publicly committed to delivering a leaner, more “efficient” FDA. That context inevitably influences both enthusiasm for, and resistance to, its use.
As of December 1, 2025, there are no publicly reported quantitative error rates comparing Elsa-assisted versus traditional review, nor any data on its impact on approval outcomes. Any effect on the broader regulatory critical path – for example, the 610-month PDUFA review window or typical 510(k) decision times – has yet to be reflected in public performance reports.
