This FDA CDRH guidance document provides recommendations for the content and submission of human factors information in premarket medical device applications including 510(k) and PMA submissions. The guidance specifies required usability engineering documentation based on applicable standards such as IEC 62366-1, risk analysis methodologies, labeling safety considerations, and validation evidence. It establishes expectations for human factors evaluation data components to support FDA's regulatory review of device safety and effective use in intended user populations and use environments.

FDA CDRH provides comprehensive FAQs addressing regulatory classification and oversight of clinical decision support software (CDSS). The guidance clarifies distinctions between software qualifying as a medical device under FDA jurisdiction versus informational tools falling outside regulatory scope. Key topics include risk assessment methodologies, software development and validation requirements aligned with IEC 62304 standards, cybersecurity considerations, and clinical effectiveness evaluation. This reference document serves as essential regulatory interpretation for manufacturers developing CDSS and healthcare IT professionals navigating FDA's evolving software oversight framework.

The FDA CDRH remains vigilant in ensuring the reliability of data submitted in support of medical device submissions. The Agency has determined that CCIC Huatongwei International Inspection (Suzhou) Co., Ltd. generated false, copied, or otherwise invalid test data submitted to FDA across multiple studies. Consequently, all study data from this testing facility will be rejected until the firm adequately addresses these data integrity issues. Device manufacturers and sponsors are reminded to conduct rigorous evaluation and oversight of third-party testing facilities to ensure compliance with FDA data integrity standards and Good Laboratory Practice requirements.

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.

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.

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.