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.

The FDA CDRH issued final guidance on human factors information for medical device marketing submissions, establishing a risk-based framework applicable to 510(k) premarket notifications, De Novo requests, premarket approval applications (PMAs), and humanitarian device exemption (HDEs). The guidance clarifies expectations for human factors documentation, enabling manufacturers to provide appropriately scaled submissions based on device risk. By streamlining requirements, the guidance reduces regulatory burden while maintaining submission quality and ensuring proper risk mitigation in device design and usability.

This final guidance document assists sponsors and other stakeholders in collecting, validating, and integrating patient preference information (PPI) to support regulatory decision-making across the complete product lifecycle. The document specifies methodological approaches to study design, data validation, and strategic incorporation of PPI throughout development, premarket review, and post-market phases. A reference document for demonstrating patient-centered evidence in device submissions.

This final guidance from FDA CDRH provides comprehensive premarket recommendations for medical devices bearing weight loss indications. The document addresses device classification, non-clinical and clinical testing protocols, performance standards, and labeling requirements. Manufacturers should consult this guidance when designing clinical study protocols and compiling premarket submissions to ensure alignment with FDA's current expectations for evidence generation and regulatory documentation.

Replaces the September 2023 final guidance. Title updated from "Quality System" to "Quality Management System" reflecting the QMSR final rule (21 CFR Part 820, effective February 2, 2026), which incorporates ISO 13485:2016 by reference. Core requirements for SPDF, premarket cybersecurity documentation, SBOM submission, and CVD policy remain. Adds terminology (Threat surface, Quality of Service) in Appendix 5.

FDA final guidance (Feb 2026) on risk-based assurance for software used in medical device production and quality management systems. Supersedes the September 24, 2025 version, with the title updated from "Quality System Software" to "Quality Management System Software" to align with the QMSR (21 CFR Part 820 / ISO 13485:2016 harmonization effective February 2, 2026). Replaces Section 6 of the 2002 GPSV. Does NOT apply to SaMD/SiMD.

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.

This FDA final guidance (December 2024) establishes the regulatory framework for Predetermined Change Control Plans (PCCPs) enabling efficient modification management for AI-enabled medical device software. The PCCP mechanism, authorized under FDORA Section 515C, permits manufacturers to implement specified software modifications without submitting supplemental premarket applications, provided modifications remain within the FDA-approved change control plan. Manufacturers must establish comprehensive PCCPs describing anticipated modification categories, methodologies for implementing changes while maintaining safety and effectiveness, and impact assessment procedures demonstrating that modifications do not adversely affect device performance or patient safety. Each PCCP submission must include three essential elements: clear descriptions of modifications covered by the plan, detailed methodologies and procedures for implementing modifications, and systematic impact assessment approaches demonstrating continued compliance with original approval specifications. The guidance specifies that PCCPs must align with 21 CFR Part 820 (Quality Management System Regulation) change management requirements, ensuring integration within broader quality system frameworks. This mechanism substantially reduces regulatory burden while maintaining robust oversight of AI algorithm modifications. Manufacturers should carefully define PCCP scope to encompass anticipated algorithm refinements while excluding modifications requiring comprehensive re-validation.