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# FDA Weighing Certification of AI Devices That Learn: Report
- URL: https://www.fdaweb.com/fda-weighing-certification-of-ai-devices-that-learn-report/
- Published: 2019-05-07T12:00:00.000Z
- Updated: 2026-09-15T01:25:15.000Z
- Author: David McFarland
- Tags: Devices, #legacy-id-D5144052

FDA has already approved so-called “locked” medical devices that rely on artificial intelligence, and now is trying to determine how to certify devices that learn and evolve. *Roll Call* [reports](http://www.rollcall.com/news/fda-grapples-with-living-medical-devices?ref=fdaweb.com) the agency is weighing how to assess and certify devices powered by artificial intelligence that continuously adapt to new symptoms presented by patients and learn how to make accurate diagnoses.

In an interview with *Roll Call*, CDRH director of digital health **Bakul Patel** said FDA is pivoting to new ways of assessing devices driven by machine learning and artificial intelligence because traditional approaches don’t apply to such new machines. “It’s a living thing to some degree,” Patel said, “so having a concept of authorizing it to go to market and waiting for things to happen and then doing a review seems to be outdated.”

The article says FDA is seeking comments on a 4/2019 white paper outlining a total product lifecycle approach that focuses on processes, quality control, testing, and the organizational culture of a maker of such medical devices rather than the typical static assessment of a piece of equipment. In the white paper, the agency said such an approach “would provide reasonable assurance of safety and effectiveness throughout the lifecycle of the organization and products so that patients, caregivers, healthcare professionals, and other users have assurance of the safety and quality of these products.” The proposal calls for assessing the device maker’s overall culture, starting with an assessment of its machine-learning practices, including the kinds of data chosen to train and fine-tune algorithms, how the manufacturer intends to turn the training model into a production one, the process used to monitor and evaluate performance of the model once it’s deployed, and how the company will take the real-world data to retrain its model.