FDA Draft Guidance: Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning (AI/ML)-Enabled Device Software Functions

With the rapid, and what some are calling hasty and precipitous, growth in artificial intelligence applications, and especially those in the medical field, the FDA is recognizing in its most recent draftguidance on the topic, that machine learning-enabled device software functions (ML-DSFs) are expected to learn and undergo iterative phases of development as the systems learn. As such, the FDA wants to ensure that they continue to provide important support to healthcare providers while at the same time ensuring the safety and efficacy of the product. The Introduction is pretty straightforward, but much of the very detailed body of the document relies on an understanding of the device approval process (which I don’t have). So for those of you interested in AI, ML, devices and FDA approval – please read the draft guidance.

 

From the Introduction:

FDA has a longstanding commitment to develop and apply innovative approaches to the regulation of medical device software and other digital health technologies to ensure their safety and effectiveness. As technology continues to advance all facets of healthcare, medical software incorporating artificial intelligence (AI), and specifically the subset of AI known as machine learning (ML) (henceforth referred to as machine learning-enabled device software functions or ML-DSFs), has become an important part of many medical devices. This draft guidance is intended to provide a forward-thinking approach to promote the development of safe and effective medical devices that use ML models trained by ML algorithms.

 

ML-enabled technologies have the potential to transform healthcare by deriving new and important insights from the vast amount of data generated during the delivery of healthcare every day. Medical device manufacturers are using ML technologies to innovate their products to better assist healthcare providers and improve patient care. Examples of ML applications in medicine include earlier disease detection and diagnosis, development of personalized diagnostics and therapeutics, and development of assistive functions to improve the use of devices with the goal of improving user and patient experience.

 

FDA recognizes that the development of ML-DSFs is an iterative process. This draft guidance proposes a least burdensome approach to support iterative improvement through modifications to an ML-DSF while continuing to provide a reasonable assurance of device safety and effectiveness. As such, this draft guidance demonstrates FDA’s broader commitment to developing innovative approaches to the regulation of device software functions as a whole. Specifically, this draft guidance provides recommendations on the information to be included in a Predetermined Change Control Plan (PCCP) provided in a marketing submission for an ML- DSF. This draft guidance recommends that a PCCP describe the planned ML-DSF modifications; the associated methodology to develop, implement, and validate those modifications; and an assessment of the impact of those modifications. The PCCP is reviewed as part of a marketing submission to ensure the continued safety and effectiveness of the device without necessitating additional marketing submissions for implementing each modification described in the PCCP.