A Comprehensive Labeling Framework for Artificial Intelligence (AI)/Machine Learning (ML)-Based Medical Devices: From AI Facts Labels to a Front-Of-Package AI Labeling System—Lessons Learned From Food Labeling

Medical Artificial Intelligence (AI) is rapidly transforming healthcare. The U.S. Food and Drug Administration (FDA) has already authorized the marketing of over one thousand AI/Machine Learning (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 from 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 details on their data sets. 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 suggests a comprehensive labeling framework for AI/ML-based medical devices. It argues that valuable lessons can be learned from food labeling and applied in the context of AI/ML-based medical devices. In particular, it argues that there is not only a need for regulators 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.