FDA CDRH provides regulatory guidance for artificial intelligence-enabled medical devices, addressing the unique oversight considerations for AI/machine learning-based Software as a Medical Device (SaMD). The resource covers quality management systems, validation methodologies, performance monitoring, and cybersecurity requirements aligned with IEC 62304 standards. It serves as a reference document for manufacturers developing AI-integrated medical devices authorized for U.S. marketing, emphasizing transparency, safety evaluation, and ongoing post-market surveillance protocols.

The FDA has updated its comprehensive searchable databases of medical devices incorporating digital health technologies authorized for U.S. marketing. These updated lists encompass artificial intelligence and machine learning-enabled devices, augmented and virtual reality medical devices, and sensor-based digital health technology solutions. This resource provides manufacturers, healthcare professionals, and regulatory stakeholders transparent access to cleared and approved AI/digital health-integrated medical devices, supporting informed decision-making and regulatory compliance efforts while facilitating continued innovation in the digital health medical device sector.

Final guidance clarifying the regulatory status of Clinical Decision Support (CDS) software under Section 520(o) of the FD&C Act. Defines criteria distinguishing device CDS (subject to FDA oversight) from non-device CDS (exempt). Addresses application to AI/ML-based CDS functions and provides examples across clinical specialties.

This FDA draft guidance (2025) provides lifecycle management and premarket submission recommendations for software incorporating artificial intelligence and machine learning (AI/ML) technologies. The document addresses critical AI-specific considerations including training data management, algorithm performance monitoring, software modification processes, and predetermined change control planning. Manufacturers should establish robust procedures for documenting training and validation datasets, monitoring real-world performance against predetermined performance specifications, and implementing planned modifications without requiring submission of new premarket applications when modifications fall within pre-approved change control plans. The guidance specifically addresses adaptive algorithms that modify behavior based on accumulated clinical experience, establishing frameworks for distinguishing routine algorithm refinement from material modifications requiring FDA notification. Post-market performance evaluation plans should establish metrics for ongoing algorithm performance assessment across diverse patient populations and clinical settings. The document remains under comment collection, with FDA inviting stakeholder input on implementation feasibility and technical approaches. Manufacturers of AI/ML-enabled medical devices should actively monitor guidance finalization and incorporate recommendations into development strategies to facilitate efficient regulatory approval pathways.