The Food and Drug Administration announced Tuesday that it is developing a framework for regulating artificial intelligence products used in medicine that continually adapt based on new data.
The agency’s outgoing commissioner, Scott Gottlieb, released a white paper that sets forth the broad outlines of the FDA’s proposed approach to establishing greater oversight over this rapidly evolving segment of AI products.
It is the most forceful step the FDA has taken to assert the need to regulate a category of artificial intelligence systems whose performance constantly changes based on exposure to new patients and data in clinical settings. These machine-learning systems present a particularly thorny problem for the FDA, because the agency is essentially trying to hit a moving target in regulating them.
The white paper describes criteria the agency proposes to use to determine when medical products that rely on artificial intelligence will require FDA review before being commercialized.
The review may examine the underlying performance of a product’s algorithms, a manufacturer’s plan to make modifications, and the manufacturer’s ability to manage the risks associated with any modifications.
“A new approach to these technologies would address the need for the algorithms to learn and adapt when used in the real world,” Gottlieb wrote in a statement accompanying the white paper. “It would be a more tailored fit than our existing regulatory paradigm for software as a medical device.”
The paper is the first step in a monthslong process in which the FDA will collect input from the public and a variety of stakeholders in medicine before finalizing a policy on regulating adaptive AI systems.