Findings
AI/ML technologies offer dynamic learning capabilities but require careful regulation to ensure safety and effectiveness.
The FDA recognizes that traditional regulatory paradigms may not align with the adaptive nature of AI/ML and is developing frameworks to address this.
Guidance documents, such as the AI/ML SaMD Action Plan and predetermined change control plan (PCCP) recommendations, provide a structured approach for handling software updates.
Collaboration across FDA centers (CDRH, CBER, CDER) facilitates consistent regulatory practices for AI/ML across medical products.
Transparency and real-world data integration are key focuses in regulating AI/ML technologies.

Recommendations
Manufacturers should use FDA’s premarket pathways, including 510(k), De Novo, or PMA, for AI/ML-enabled SaMD.
Apply Good Machine Learning Practices (GMLP) during development to ensure algorithm reliability, transparency, and patient safety.
Include a predetermined change control plan (PCCP) in submissions to allow for iterative updates without requiring resubmissions.
Follow lifecycle management practices to maintain AI/ML system performance after deployment.
Engage with FDA early in development to align on appropriate regulatory strategies for novel AI/ML implementations.

Regulatory Considerations
AI/ML-driven SaMD updates may require premarket review, depending on the significance of changes and associated risks.
The FDA has outlined principles for transparency, including clear labeling and documentation of AI/ML system capabilities and limitations.
Guidance documents like the “Good Machine Learning Practice” and “Marketing Submission Recommendations for PCCP” should be followed for compliance.
Collaboration between FDA centers ensures alignment on the use of AI in combination products and broader healthcare applications.
Lifecycle management strategies must account for real-world data to ensure continuous learning and safe AI/ML system updates.