Every few weeks, another imaging AI secures FDA clearance, and the announcement is treated as the end of the evaluation. For a hospital deciding whether to deploy that tool, clearance is closer to the beginning. It tells you the model met a regulatory bar on the data the manufacturer chose. It does not tell you how it will perform on your patients.
The questions that matter are the ones clearance does not answer. What population was the validation cohort drawn from, and how does it compare to mine? What is the performance on the subgroups my institution actually serves? How does the tool behave on the edge cases, the rare anatomy, the comorbid patient who never appears in a clean training set?
I have watched cleared algorithms perform beautifully in the literature and then degrade on a local population that simply did not look like the development data. That is not fraud or failure on the vendor’s part. It is the predictable consequence of treating a clearance as a guarantee of generalisability, which it was never designed to be.
For cardiac and procedural imaging, the responsible posture is local validation before reliance. Run the cleared tool against your own labelled cases. Measure where it agrees and where it diverges from your readers. Decide deployment on that evidence, not on the press release.
Clearance earns a tool the right to be evaluated in your institution. It does not earn it the right to be trusted on your patients. The hospitals that internalize that distinction will deploy imaging AI safely. The ones that treat the FDA logo as the final word will eventually clear something onto a patient it was never validated for.
Dr. Khalpey is chief medical AI officer at Atari AI, chair of applied clinical AI at the Atari AI Foundation and director of Khalpey AI Lab. He is a Fortune 500 advisor and author of The AI Prescription, an exploration into how AI enhances human expertise in medicine.
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