Gen AI Drafts Radiology Reports

I keep thinking about what happened this week in radiology.

The FDA granted Breakthrough Device Designation to two generative AI tools that do not just detect findings on a chest X-ray. They draft the radiology report.

Aidoc’s First Read. Cognita, the Stanford-founded startup now owned by Radiology Partners. Both cleared this threshold in the same week.

This is not incremental. This is a category change. 🔬

For the past decade, radiology AI meant one thing: a model looks at an image and highlights a spot. A radiologist still does the interpretation. A radiologist still writes the report. The AI was a tool, not an author.

That model just changed.

Generative AI, driven by large vision-language models, can now process the entire image and draft the findings. The radiologist reviews and signs. But the cognitive work of narrating what the scan shows is shifting to the machine.

The FDA itself acknowledged the weight of this. Breakthrough Device Designation is reserved for technologies that significantly advance diagnosis of serious conditions AND represent an unmet clinical need. The agency is saying: this matters, and we need to move faster on it.

Here is the context that makes this urgent. There are now 1,524 FDA-cleared radiology AI algorithms as of mid-2026. The agency cleared 68 new ones in just the first three months of this year. But almost all of those tools were built on the old model, detection and triage, not generation.

Aidoc alone already holds 18 FDA clearances and is deployed across more than 150 U.S. health systems. First Read is their second Breakthrough Designation in under a year. These are not moonshots from a garage startup. This is production infrastructure moving toward report authorship.

At Oatmeal Health, we live in this world every day. We build AI for lung cancer screening on low-dose chest CT. Our work is about catching what gets missed, finding the nodule that falls through the cracks before it becomes stage four disease. And I will tell you this plainly: the question of where AI ends and the radiologist begins is not theoretical for us. It is the center of every product decision we make.

What concerns me is not the technology. The technology is ready. What concerns me is that the validation frameworks, the liability structures, and the reimbursement models were all designed for detection AI, not for AI that narrates. We are now handing a pen to the machine, and the legal and clinical infrastructure has not caught up. 🎯

The biggest risk here is not that generative AI drafts a bad report. It is that we deploy these tools at scale before we have built the accountability layer that tells us what to do when it does.

Generative AI in radiology is not coming. It is here, and the FDA just put its hand up to say it is worth accelerating.

To every chief radiology officer, CMO, and health system AI lead reading this: the time to build your governance framework for generative report AI is right now, before your vendor pitches you one.

Where does your system stand on who is responsible when a generative AI draft contains an error that a busy radiologist misses on sign-off?

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