Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning

On April 2 the Food and Drug Administration (FDA) released a discussion paper and request for feedback from stakeholders by June 3 on a proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML) -based software as a medical device (SaMD). SaMD is software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device. Under the Federal Food, Drug & Cosmetic Act, FDA considers medical purpose as those purposes that are intended to treat, diagnose, cure, mitigate, or prevent disease or other conditions. The foundation of this paper builds on the fact that AI, specifically ML, are techniques used to design and train software algorithms to learn from and act on data. The proposed regulatory framework speaks to the reality that adaptive AI/ML technologies are constantly evolving, and have the potential to adapt and optimize device performance in real-time to continuously improve healthcare for patients.

The underlying issue raised is, what happens when these medical devices are continuously learning to the extent that there is a modification that may require a premarket submission for an algorithm change? This inquiry prompted the FDA to reimagine an approach to premarket review for AI/ML based SaMD, while allowing the software to continue to learn and evolve over time to advance and improve patient care without the delay of traditional means of medical device regulation that are misaligned and are slow to keep up with the speed of developing adaptive AI/ML technologies. The FDA points out the need for a new total product lifecycle (TPLC) regulatory approach that facilitates a rapid cycle of product improvement and allows these devices to continually improve while providing effective safeguards. While speaking to the quality systems and good machine learning practices expected of every medical device manufacturer, the FDA additionally proposes a framework for modifications to AI/ML-based SaMD that build on existing principles related to Medical device risk safeguards as well as describes an innovative approach that may require additional statutory authority to implement completely.