Devices

Comments to FDA on ‘Artificial Intelligence’

GE Healthcare says artificial intelligence/machine learning (AI/ML) “is set to revolutionize healthcare. Device manufacturers are embracing AI/ML to create new medical devices and transform the way medical devices function in the healthcare environment.” Responding to an FDA discussion paper and request for feedback, the company says the total product lifecycle approach described by the agency would allow a more efficient application of regulatory oversight with the potential to accelerate market access. GE also points out that while FDA proposes limiting the scope of the program to software as a medical device (SaMD), AI/ML and its ability to learn and adapt is also applicable to software in a medical device and suggests that the proposed regulatory framework apply to all AI/ML applications that meet the definition of medical device.

Pharmaceutical Research and Manufacturers of America says it supports FDA’s proposed framework to regulating AI/ML-based SaMD and makes recommendations to help the agency address the issues.

In its letter, the National Center for Health Research provides recommendations and issues of concern, including that Good Machine Learning Practices also require transparency of the underlying data used to build or refine any AI/ML-based SaMD product, and that FDA must clearly explain how the proposed regulatory framework will protect patients given the scale, scope, and complexity of recommendations made by AI/ML-based SaMD.

The American Medical Association says FDA should (1) clarify if the discussion paper is specifically addressing machine learning; (2) provide appropriate balance concerning benefits and risks; (3) tie developer goals to patient outcomes; and (4) standardize nomenclature and terminology.

In its comment, Novartis says the framework “provides important information for developers of AI/ML-based SaMD, although largely from a high-level perspective. (XXX WHERE TO END THE DIRECT QUOTE?? XXX)The company provided general comments on cross-Center coordination, scope of the proposed framework, risk categorization, modification types, SaMD pre-specifications and algorithm change protocol, Good Machine Learning Practices, and real-life performance.

The Duke Margolis Center for Health Policy says there are areas in need of additional nuance or discussion, including the lack of definitive and universal definitions of AI-related terminology.

The Medical Imaging & Technology Alliance says the FDA discussion paper relies on four assumptions that the group does not agree with: (1) terms related to AI, like ML and continuous learning are well-defined and widely understood; (2) the software precertification pilot program model is a necessary and sufficient base from which to build a more encompassing regulatory model around all AI software; (3) existing regulatory review processes are insufficient for AI software; and (4) the total product lifecycle is interdependent with the pre-certification program.

Finally, AdvaMed answered specific technical questions posed by FDA.

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