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# FDA’s Central Role in AI Regulation
- URL: https://www.fdaweb.com/fdas-central-role-in-ai-regulation/
- Published: 2024-10-16T12:00:00.000Z
- Updated: 2026-09-14T14:44:06.000Z
- Author: David McFarland
- Tags: FDA Policy/General, Devices, #legacy-id-D5157994

FDA commissioner **Robert Califf** and two agency colleagues say that historic advances in artificial intelligence (AI) applied to biomedicine and healthcare “must be matched by continuous complementary efforts to better understand how AI performs in the settings in which it is deployed.” Writing in a *JAMA* Special Communication, the authors [say](https://jamanetwork.com/journals/jama/fullarticle/2825146?guestAccessKey=20a721c9-132c-4728-b6df-996d38f868ad&utm%5Fsource=twitter&utm%5Fmedium=social%5Fjama&utm%5Fterm=14968539560&utm%5Fcampaign=article%5Falert&linkId=623886833) the effort “will entail a comprehensive approach reaching far beyond FDA, spanning the consumer and healthcare ecosystems to keep pace with accelerating technical progress. If not, there is a risk that AI could disappoint similar to other general-purpose technologies deployed in healthcare settings or even create significant harm if untended models’ performance deteriorates or focuses on financial return without adequate attention to impact on clinical outcomes.”

The article says strong oversight by FDA and other agencies aims to protect the long-term success of regulated products by maintaining a high grade of public trust in the regulated space.

It looks at FDA’s history of regulating AI-enabled products and discusses these concepts pertinent to FDA regulation of AI:

- AI regulation within the broader U.S. government and global context;
- keeping up with the pace of change in AI;
- flexible approaches across the spectrum of AI models;
- the use of AI in medical product development;
- preparing for the unknowns of large language models and generative AI;
- the central importance of AI life cycle management;
- the responsibilities of regulated industries;
- maintaining robust supply chains;
- finding the balance between big tech, start-ups, and academia; and
- the tension between using AI to optimize financial returns vs. improving health outcomes.