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# Half of AI Devices Lack Clinical Validation Data: Research
- URL: https://www.fdaweb.com/half-of-ai-devices-lack-clinical-validation-data-research/
- Published: 2024-08-26T12:00:00.000Z
- Updated: 2026-09-14T14:39:34.000Z
- Author: David McFarland
- Tags: Devices, #legacy-id-D5157651

A group of researchers led by Duke Heart Center’s **Sammy Chouffani El Fassi** analyzed over 500 recent medical artificial intelligence-driven devices that showed about half of them lacked reported clinical validation data. Their findings [were published](https://www.nature.com/articles/s41591-024-03203-3?ref=fdaweb.com) in *Nature Medicine* on 8/26.

“Although AI device manufacturers boast of the credibility of their technology with FDA authorization, clearance does not mean that the devices have been properly evaluated for clinical effectiveness using real patient data,” Chouffani El Fassi is quoted in a [release](https://www.eurekalert.org/news-releases/1055604?ref=fdaweb.com). “With these findings, we hope to encourage the FDA and industry to boost the credibility of device authorization by conducting clinical validation studies on these technologies and making the results of such studies publicly available.”

The researches found that the average number of medical AI device authorizations by FDA per year has increased from two to 69 since 2016, which they note shows the growth in this area. The majority of AI medical technologies are being used to assist physicians with diagnosing abnormalities in radiological imaging, pathologic slide analysis, dosing medicine, and predicting disease progression, they say.

Of the 521 device authorizations, 144 were labeled as “retrospectively validated,” 148 were “prospectively validated,” and 22 were validated using randomized controlled trials, the researchers say. Most importantly, 226 of 521 (43%) of the AI medical devices lacked published clinical validation data. “A few of the devices used ‘phantom images’ or computer-generated images that were not from a real patient, which did not technically meet the requirements for clinical validation,” they say, adding that the latest (2023) FDA draft guidance does not clearly distinguish between different types of clinical validation studies in its recommendations to manufacturers.

“We shared our findings with directors at the FDA who oversee medical device regulation, and we expect our work will inform their regulatory decision-making,” said Chouffani El Fassi. “We also hope that our publication will inspire researchers and universities globally to conduct clinical validation studies on medical AI to improve the safety and effectiveness of these technologies. We’re looking forward to the positive impact this project will have on patient care at a large scale.”