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# CDRH Should Increase AI Transparency in Devices: PEW
- URL: https://www.fdaweb.com/cdrh-should-increase-ai-transparency-in-devices-pew/
- Published: 2022-02-18T12:00:00.000Z
- Updated: 2026-09-14T17:33:18.000Z
- Author: David McFarland
- Tags: Devices, #legacy-id-D5151202

CDRH should increase transparency by requiring more and better information on artificial intelligence (AI)-enabled tools in its approvals public database. “Currently, the details that companies publicly report about their products vary,” according to a [post](https://www.pewtrusts.org/en/research-and-analysis/articles/2022/02/18/why-fda-must-increase-transparency-of-medical-devices-powered-by-artificial-intelligence?ref=fdaweb.com) by Pew Charitable Trusts health care products director **Liz Richardson**. “For example, in an [analysis](https://www.statnews.com/2021/02/11/breast-cancer-disparities-artificial-intelligence-fda/?ref=fdaweb.com) of public summaries for the 10 FDA-cleared AI products for breast imaging, only one provided information about the racial demographics of the data used to validate the product. Requiring developers to publicly report basic demographic information — and where appropriate, data on how the product performed in key subgroups — could help providers and patients select the most appropriate products. This is especially important when treating conditions with disparate impacts on underserved populations, such as breast cancer, a disease more likely to be fatal for black women.”

Richardson said AI data in device labeling should mirror requirements for drug labeling, and the agency could also require device developers to provide more detailed information on product labels so that these tools can be properly evaluated before being purchased by health care facilities or patients. “Researchers at [Duke University](https://www.statnews.com/2020/10/05/duke-artificial-intelligence-hospital-medicine/?ref=fdaweb.com) and the [Mayo Clinic](https://www.healthcareitnews.com/news/addressing-ai-bias-algorithmic-nutrition-label?ref=fdaweb.com) have suggested an approach akin to a nutrition label that would describe how an AI tool was developed and tested and how it should be used,” she wrote. “This would allow end users to better assess products before they are used on patients. The information could also be integrated into an institution’s electronic health record system to help make the data easily available for busy providers at the point of care.”