1,357 AI medical devices are cleared and just 3 were tested on actual patients
Key takeaways
- A University of Toronto and MIT audit found only 3 of 1,357 FDA cleared AI medical devices had registered trials measuring patient outcomes.
- 1,059 of the devices, about 78 percent, are radiology tools.
- Clearance shows a device meets regulatory requirements; it is not evidence that patients do better with it.
Last updated: 5 October 2026
Three out of 1,357. That is how many FDA cleared AI medical devices had registered trials measuring what happened to patients, according to an audit from the University of Toronto and MIT published in PLOS Digital Health.
It works out to about 0.2 percent. The other devices were cleared, which is a different thing from being shown to help. The audit looked only at registered trials, so it counts studies that were formally set up to measure patient outcomes.
What the audit found
Of the 1,357 cleared devices, 1,059 are radiology tools, roughly 78 percent. These systems look at scans and flag findings, so most were likely cleared on the strength of how accurately they read images against a reference.
Accuracy on images is a useful measure, but it is not the same as a better outcome. A tool can spot a shadow reliably and still change nothing about whether a patient is treated sooner, recovers faster or avoids harm.
Why clearance gets misread
Clearance is a regulatory decision about whether a device meets the bar to be sold. In plain terms, it asks whether the device is safe and similar enough to something already on the market, not whether patients do better with it.
The risk is that hospitals, buyers and patients read cleared as proven. When the paperwork says authorised and the marketing says AI, the missing outcome data is easy to overlook.
What to watch for next
Much of the AI coverage we run is about scale, in gigawatts, valuations and parameters, as in our look at how much power AI data centers use. This story sits at the opposite end, with a handful of trials.
The same pattern of deployment running ahead of testing shows up elsewhere, including the OpenAI agent incident. The practical question to ask of any clinical AI tool is simple: where is the study showing patients did better?
