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The FDA is inviting stakeholder feedback on how generative AI-enabled medical devices should be evaluated and monitored. Its new discussion paper examines risk assessment, premarket evaluation, post market monitoring, and other regulatory considerations for systems whose outputs may be dynamic or context-dependent. The paper is intended only to stimulate discussion. It is neither draft nor final guidance and does not establish new regulatory expectations. For accredited CME/CE professionals, the FDA’s questions might help identify learning gaps, guide faculty content, and shape case-based activities that teach clinicians how to evaluate evidence, oversee generative-AI medical devices in practice, and recognize when a device’s performance may require reassessment. Feedback from clinicians, researchers, manufacturers, and other interested parties is due October 19, 2026.
ACCME’s 30-minute rule is not simply a scheduling requirement; it also governs the educational space used for accredited education. Under Standard 5, marketing, exhibits, and certain nonaccredited education involving ineligible companies or individuals with unmitigated financial relationships cannot occur in the educational space during the 30 minutes before or after an accredited activity. Further, learners must be able to distinguish easily between the two. Conversely, if these ancillary activities are held in a different space, completely separate from the accredited CE educational space, the 30-minute rule does not apply. A comprehensive review of the full learner experience, whether in person, virtual, or hybrid, can help ensure that accredited education remains appropriately separated from marketing and nonaccredited content by time and space, as applicable.
As CME/CE organizations consider governance measures for the use of AI within business operations, the NIST AI Risk Management Framework, developed by the National Institute of Standards and Technology, offers a useful structure. The voluntary framework organizes AI risk management around four functions – Govern, Map, Measure, and Manage – with governance intentionally cutting across the entire AI lifecycle. This structure can help turn broad AI principles into operational practices: define accountability, assess risks in the context of specific use cases, evaluate performance and potential harms, and document how identified risks are addressed. NIST emphasizes that risk tolerance is context-specific and that AI risk management should integrate with existing enterprise processes rather than be treated as a standalone technology exercise.
