On October 14, 2021, the U.S. Food and Drug Administration ("FDA" or the "Agency") held a virtual workshop entitled, Transparency of Artificial Intelligence ("AI")/Machine Learning ("ML")-enabled Medical Devices. The workshop builds upon previous Agency efforts in the AI/ML space. Back in 2019, FDA issued a discussion paper and request for feedback called, Proposed Regulatory Framework for Modifications to AI/ML-Based Software as a Medical Device ("SaMD"). To support continued framework development and to increase collaboration and innovation between key stakeholders and specialists, FDA created the Digital Health Center of Excellence in 2020. And, in January 2021, FDA published an AI/ML Action Plan, based, in part, on stakeholder feedback to the 2019 discussion paper.
As part of the G7's health track artificial intelligence (AI) governance workstream 2021, member states committed to the creation of 2 deliverables on the subject of governance: These papers are complementary and should therefore be read in combination to gain a more complete picture of the G7's stance on the governance of AI in health. This paper is the result of a concerted effort by G7 nations to contribute to the creation of harmonised principles for the evaluation of AI/ML-enabled medical devices, and the promotion of their effectiveness, performance, safety and ethicality. A total of 3 working group sessions were held to reach consensus on the content of this paper. The rapid emergence of AI/ML-enabled medical devices provides novel challenges to current regulatory and governance systems, which are based on more traditional forms of Software as a Medical Device (SaMD). Regulators, international standards bodies[footnote 2] and health technology assessors across the world are grappling with how they can provide assurance that AI/ML-enabled medical devices are safe, effective and performant – not just under test conditions but in the real world.
The U.S. Food and Drug Administration recently partnered with Health Canada and the UK's Medicines and Healthcare products Regulatory Agency to issue guiding principles to align efforts and standards for artificial intelligence and machine learning medical device development in health care. "The FDA believes that artificial intelligence and machine learning technologies have the potential to transform health care by deriving new and important insights from the vast amount of data generated during the delivery of health care every day," said Jim McKinney, public affairs specialist at the FDA, in an email to The Well News. McKinney said the 10 guiding principles grew out of collaborative discussions with Health Canada and MHRA, and learning from several sectors that applied AI and ML technologies for years and have developed good practices that can be readily applied to the medical device industry. Evidence from published information, expert and other public perspectives and review experience was used to develop the guiding principles that will be used by the agency to lay the foundation for the development of Good Machine Learning Practice, which will unify international efforts for medical device development. Over the past decade the FDA has reviewed and authorized a growing number of devices legally marketed with machine learning and expects this trend to continue.
The Food and Drug Administration recently sought comments on the role of transparency for artificial intelligence and machine learning-enabled medical devices. The FDA invited comments in follow up to a recent workshop on the topic. The workshop was part of a series of efforts the FDA has had in this space. These include its Digital Health Center of Excellence and a five-part Action Plan for AI and machine-learning enabled medical devices. As part of the action plan, the FDA indicated it wants to issue guidance on software learning over time and help the industry be "patient-centered."