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# Device AI Facts Labeling Needed: Paper
- URL: https://www.fdaweb.com/device-ai-facts-labeling-needed-paper/
- Published: 2025-07-09T12:00:00.000Z
- Updated: 2026-09-14T15:16:11.000Z
- Author: David McFarland
- Tags: Devices, #legacy-id-D5159475

A University of Illinois law school scholar who specializes in the ethical and legal challenges of artificial intelligence (AI) for health care says FDA needs to develop labeling standards for AI-powered medical devices. A university [news release](https://news.illinois.edu/paper-fda-needs-to-develop-labeling-standards-for-ai-powered-medical-devices/?ref=fdaweb.com) says **Sara Gerke** suggests that the labeling follow the format of mandatory nutrition labeling on food products.

“The current lack of labeling standards for AI- or machine learning-based medical devices is an obstacle to transparency in that it prevents users from receiving essential information about the devices and their safe use, such as the race, ethnicity, and gender breakdowns of the training data that was used,” Gerke writes. “One potential remedy is that FDA can learn a valuable lesson from food nutrition labeling and apply it to the development of labeling standards for medical devices augmented by AI.”

Gerke says that many AI-powered devices are based on deep learning, a subset of machine learning, and are essentially “black boxes.” She says their reasoning for why the tools make a particular recommendation, prediction, or decision is hard, if not impossible, for humans to understand. Further, she says, it’s difficult to assess a new technology’s reliability and efficacy once it’s been implemented in a hospital.

“Normally,” she says, “you would need to revalidate the tool before deploying it in a hospital because it also depends on the patient population and other factors. So it’s much more complex than just plugging it in and using it on patients.”

A comprehensive labeling framework for AI-powered medical devices should consist of four components, Gerke concludes: two AI Facts labels, one front-of-package AI labeling system, the use of modern technology like a smartphone app, and additional labeling. “Such a framework,” she says, “includes things from as simple as a ‘trustworthy AI’ symbol to instructions for use, fact sheets for patients, and labeling for AI-generated content. All of which will enhance user literacy about the benefits and pitfalls of the AI, in much the same way that food labeling provides information to consumers about the nutritional content of their food.”