AI Could Cut Medical Device Recalls by Nearly a Third, Study Finds

A human-algorithm approach could cut FDA review workload by 40.5% while improving recall detection by 32.9%

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.

“AI is most useful in this setting when it works alongside human judgment, not when it tries to replace it,” said Soroush Saghafian, co-author. “An algorithm can systematically identify patterns in thousands of devices and their predicate histories, but there will always be cases where the evidence is ambiguous, or the stakes are too high for an automated decision. By reserving those cases for expert review, the FDA can use its human expertise where it matters most while making the overall process more efficient.”

The system works with predicate devices, or devices that may look similar enough, or “substantially equivalent,” to an existing approved device on the market that regulators could be reasonably confident is safe and effective. Researchers sought to make the comparison system more informative by looking systematically at the history of predicate devices, particularly how old the predicate devices were and their recall status, to estimate the likelihood that the new device would eventually be recalled.

For the devices, an optimization model set thresholds in an algorithm for three categories: devices that could be cleared, devices that may carry a high enough risk to be rejected and devices in between that should be reviewed by human experts, balancing the goal of identifying potentially unsafe devices with the FDA’s limited review capacity.

“Right now, the FDA has to devote substantial resources to reviewing medical devices even when the available evidence suggests they pose relatively little risk,” said Mohammad Zhalechian, lead author. “Our results suggest that a data-driven approach could help distinguish the straightforward cases from the ones that deserve a closer look. The goal isn’t to replace FDA reviewers—it’s to give them better information about where their time and expertise can have the greatest impact.”

The study and its findings coincide with an existing regulatory conversation. In 2023, the FDA put out a draft guidance about predicate selection best practices, proposing to evaluate predicate devices based on safety track record rather than just availability. The study’s model speaks directly to that proposal by tracking predicate recall history and age, a safety track record that the FDA’s own guidance pushes for. If tools like this can inform the direction the FDA takes, the result could be more safety, more efficient use of resources, and more money saved.

About INFORMS and Management Science

INFORMS is the world’s largest association for professionals and students in operations research, AI, analytics, data science, and related disciplines, serving as a global authority in advancing cutting-edge practices and fostering an interdisciplinary community of innovation. Management Science, a leading journal by INFORMS, publishes quantitative research on management practices across organizations. INFORMS empowers its community to improve organizational performance and drive data-driven decision-making through its journals, conferences, and resources. Learn more at www.informs.org or @informs.

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