FDA Awards $1.29 Million Contract to Test Generative AI Evaluation of Radiology Reports

FDA contract puts an LLM-as-a-Jury evaluation on the regulatory bench

Cognita Imaging has been awarded an FDA contract to test whether generative AI can be used to judge other AI systems in radiology, according to coverage published September 22, 2026. 29 million in another covering the same award; the validation work is scoped across one million radiology reports. The approach positions a large language model as a panel-style evaluator, an "LLM-as-a-Jury" method, to grade AI-generated radiology outputs before they reach clinicians.

For practitioners building generative medical tools, this is the first concrete federal signal that evaluation infrastructure for generative outputs is being formalized alongside the models themselves. Teams preparing FDA submissions will need to show not only model performance but a defensible scoring methodology for any AI-on-AI review step.

A separate MICSI-PET clearance moves MR-guided neurological PET forward

In the same FDA pipeline, MICSI-PET has received FDA clearance for MR-guided neurological PET imaging, reported September 22, 2026. The clearance sits in the imaging-device lane rather than the generative-AI lane, but it shares the same day's regulatory news cycle and confirms that neurological PET workflows tied to MRI guidance are moving through review. For developers of generator-adjacent tooling — synthetic image libraries, mock radiology datasets, test identifiers — the clearance underscores a steady clinical pull for integrated imaging modalities that still depend on large volumes of realistic placeholder content for validation.

What practitioners building generative pipelines should watch next

The clearest immediate shift is that evaluation of generative medical output is being treated as a deliverable, not an afterthought. The Cognita contract asks whether an LLM panel can stand in for human radiologist review at scale, and it pays for that question to be answered with one million reports of evidence. Teams that currently rely on small human-rated sets to validate AI-generated findings should expect procurement and submission documents to start asking how scoring was done, who scored what, and whether a model was in the loop.

Anyone responsible for synthetic test data should also expect buyers to ask whether mock reports can be provenance-tagged, because evaluation frameworks will increasingly need to tell real and synthetic outputs apart. A checklist for the next review cycle should cover scoring protocol, provenance tracking on generated images, and a documented policy for any LLM-as-a-Judge step.