Artificial intelligence-enabled medical devices with no clinical validation were more likely to be the subject of recalls, according to a study published in JAMA Health Forum.
The study, published on Aug. 22, looked at 950 AI medical devices authorized by the Food and Drug Administration through November 2024. Sixty of the devices were associated with 182 recall events.
The most common causes of recalls were diagnostic or measurement errors, followed by functionality delay or loss. About 43% of all recalls also took place within one year of FDA authorization.
Tinglong Dai, lead author of the study and a professor at the Johns Hopkins Carey Business School, said the “vast majority” of recalled devices had not undergone clinical trials. For the majority of AI-enabled devices, which went through the FDA’s 510(k) pathway, clinical studies are not required.
“Unfortunately, it’s not required, and so people don’t do it,” Dai said in an interview. “So, that’s why we believe it is one of the most important drivers of the recalls.”
By comparison, the study found that devices that had gone through retrospective or prospective validation were subject to fewer recalls.
The study also found that publicly traded companies accounted for disproportionately more recall events, with public company status associated with a nearly six times higher chance of a recall event. Publicly traded companies accounted for about 53% of AI-enabled devices on the market, but they were associated with more than 90% of recall events in the study and 98.7% of recalled units.
Public companies also had a lower rate of clinical validation compared to private companies. While about 40% of recalled devices from private companies lacked validation, by comparison, about 78% of devices from larger public companies and 97% from smaller public companies had no validation.
Dai was surprised by this finding, saying that “this fundamentally has something to do with the 510(k) clearance pathway.”
The results raise concerns about the devices’ post-market safety and reliability. Dai and his co-authors recommended requiring human testing or clinical trials before a device is authorized, or incentivizing companies to conduct ongoing studies and collect real-world performance data. The pre-market and postmarket data could also help manufacturers identify and reduce device malfunctions and errors.
Dai also suggested a process where clearances may be revoked after five years if a device has no public clinical data, postmarket validation or proof that it is effective in the real world.
In 2023, the FDA issued three draft guidances to improve the 510(k) program, including recommendations around choosing appropriate predicate devices and when clinical data may be needed to demonstrate substantial equivalence. However, the guidance documents still have not been finalized.
Researchers at the Johns Hopkins Carey Business School, the Johns Hopkins Bloomberg School of Public Health and Yale School of Medicine contributed to the study. It was funded by an award from Johns Hopkins University.
