In June 2025, the Food and Drug Administration rolled out Elsa, an agency-wide generative AI assistant that officials say is already helping to speed the review of new drugs and devices and shrink weeks of paperwork into minutes. It is a vision that could reshape how lifesaving therapies reach patients, but Elsa’s first six months have exposed growing pains: internal pushback, documented hallucinations, and a more fundamental problem regulators have long overlooked: fragmented, disparate, and misaligned data standards.

Today’s new drug applications arrive at the FDA as massive, unstructured documents that can exceed 100,000 pages. Protocols, manufacturing data, and trial documentation are stitched together in largely incompatible formats with inconsistent terminology. A safety event labeled as “nausea” in one trial might appear as “gastrointestinal disorder” in another. Even when companies rely on shared dictionaries like MedDRA, they often use different versions, making it impossible to compare like with like.