FDA’s Next Frontier: Regulating Machine Learning in Clinical Decision Support Software

insideBIGDATA

Bradley Merrill Thompson, a Member of the Firm in the Health Care and Life Sciences practice, in the firm’s Washington, DC, office, authored an article in insideBIGDATA, titled “FDA’s Next Frontier: Regulating Machine Learning in Clinical Decision Support Software.”

Following is an excerpt:

Many at FDA are big fans of using machine learning in healthcare. As a research tool, machine learning offers great promise in the discovery of new drugs and other treatments.  But when machine learning algorithms become part of a regulated medical device, the unique nature of that technology creates challenges for the agency.

Machine learning is not a new subject for FDA. For years, the agency has been regulating software that uses machine learning algorithms to analyze medical images such as mammograms for potential areas of concern.  For these products, there is now a relatively well-traveled path to market that includes providing FDA with specific information about the algorithm and its training, information on the features analyzed and the models and classifiers used. Further, in their submissions, developers commonly compare the software results against three human experts to see if there is sufficient agreement.