This FDA draft guidance (2025) provides lifecycle management and premarket submission recommendations for software incorporating artificial intelligence and machine learning (AI/ML)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.