The Food and Drug Administration announced the availability of draft guidance that provides recommendations on lifecycle controls in submissions to market machine learning-enabled device software functions. In the "Marketing Submission Recommendations for a Predetermined Change Control Plan for Artificial Intelligence/Machine Learning-Enabled Device Software Functions," the FDA proposes to ensure that AI/ML-enabled devices "can be safely, effectively and rapidly modified, updated, and improved in response to new data," said Brendan O'Leary, deputy director of the Digital Health Center of Excellence in the FDA's Center for Devices and Radiological Health, in a March 30 announcement. FDA says that companies must also describe how information about modifications will be clearly communicated to users in the PCCP. The agency explains that control plans are not just intended for the AI/ML-enabled software as a medical device – "but for all AI/ML-enabled device software functions." "The approach FDA is proposing in this draft guidance would ensure that important performance considerations, including with respect to race, ethnicity, disease severity, gender, age and geographical considerations, are addressed in the ongoing development, validation, implementation and monitoring of AI/ML-enabled devices," he said.

Editor's note: This is the second part of a two-part series. The first installment can be found here. The first part installment in this series examined the benefits of Artificial Intelligence/Machine Learning (AI/ML) and noted the considerations that regulatory bodies are studying for use with AI/ML algorithms. The second and final installment explores the past and current regulations, and summarizes the latest framework proposed by the U.S. Food and Drug Administration's (FDA). While current guidance around AI/ML implementation in medical devices is lacking, the FDA is working to solve the problem.