Sara Gerke (U Illinois College Law) has posted “The Need for ‘Nutrition Facts Labels’ and ‘Front-Of-Package Nutrition Labeling’ For Artificial Intelligence/Machine Learning-Based Medical Devices – Lessons Learned From Food Labeling” (Forthcoming, Emory Law Journal (Vol. 74, 2025)) on SSRN. Here is the abstract:
Medical AI is rapidly transforming healthcare. The U.S. Food and Drug Administration (FDA) has already authorized the marketing of over 1000 AI/ML-based medical devices, and many more products are in the development pipeline. However, despite this fast development, the regulatory framework for AI/ML-based medical devices could be improved. This Article focuses on the labeling for AI/ML-based medical devices, a crucial topic that needs to receive more attention in the legal literature and by regulators like the FDA. The current lack of labeling standards tailored explicitly to AI/ML-based medical devices is an obstacle to transparency in the use of such devices. It prevents users from receiving essential information about many AI/ML-based medical devices necessary for their safe use, such as race/ethnicity and gender breakdowns of the used training data. To ensure transparency and protect patients’ health, the FDA must develop labeling standards for AI/ML-based medical devices as quickly as possible.
This Article argues that valuable lessons can be learned from food labeling and applied to labeling for AI/ML-based medical devices. In particular, it argues that there is not only a need for regulators like the FDA to develop “nutrition facts labels,” called here “AI Facts labels” for AI/ML-based medical devices, but also a “front-of-package (FOP) nutrition labeling system,” called here “FOP AI labeling system.” The use of FOP AI labels as a complement to AI Facts labels can further users’ literacy by providing at-a-glance, easy-to-understand information about the AI/ML-based medical device and enable them to make better-informed decisions about their use. This Article is the first to establish a connection between FOP nutrition labeling systems and their promise for AI/ML-based medical devices and make concrete suggestions on what such a system could look like. It also makes additional concrete proposals on other aspects of labeling for AI/ML-based medical devices, including the development of an innovative, user-friendly app based on the FOP AI labeling system as well as labeling requirements for AI/ML-generated content.