Regulatory-Grade AI Change Management: Versioning, Traceability, and Rollback for Iterative Algorithms in Medical Devices

Abstract

Managing the change management process around AI-enabled software functions embedded within other medical device products requires more than deterministic software governance frameworks. Changing how businesses manage AI in medical devices needs more than just strict rules. Regular updates to an AI model—like changes to the training data, the way the model is built, its settings, or how it processes results—can create different and unexpected risks that might not be clear from overall performance numbers. So, the rules for managing changes should involve keeping a detailed record of all changes made, creating processes that can be repeated and checked with secure signed documents, and using clear step-by-step checks with set plans for what changes are allowed. Additionally, continuous monitoring at each deployment stage, ongoing model health surveillance, and clearly defined quantitative conditions for reverting changes are essential. Organizations must also establish structured rollback procedures with traceable documentation and require formal performance testing and approval of the model both before and after any changes or rollbacks are executed. These controls should be completely included in the standard operating procedures of the quality management system to make sure that AI-enabled software works clearly, can be checked, and stays in line with the necessary rules during the entire product lifecycle.