BALTIMORE, Sept. 21, 2026 – Most medical devices do not hit the market through rigorous safety testing but by being deemed similar enough to something already approved—the "predicate" device through a mechanism known as the FDA's 510(k) pathway. But similar is not a synonym for safe. While the assumption may be that substantial equivalence is a reasonable stand-in for a safety review, a share of devices cleared with this method still get recalled, and the FDA spends valuable time reviewing devices the same way, regardless of how risky it is on paper.
A new study in Management Science finds that a human-plus-algorithm approach could help the FDA focus its limited review resources on medical devices whose safety is harder to assess, while potentially reducing future recalls. The study found that FDA could catch more unsafe devices and spend less time on the ones that don't need scrutiny, with a 40.5% reduction in review workload, 32.9% improvement in the recall rate–FDA's current recall rate is 10.3%–and up to an estimated $1.7 billion in annual healthcare savings from fewer device replacements.
Researchers from Indiana University, Harvard Kennedy School, and Emerging Health Consulting built a tool that flags which submissions are safe bets, which are risky enough to reject after a short scrutiny, and which need more careful human expert consideration. The machine-learning system estimating recall risk, and coupled with a data-driven policy, recommends which medical devices could be cleared or rejected algorithmically with minimal human oversight and which cases should be flagged for in-depth human review.
