The U.S. Food and Drug Administration is grappling with some AI-related issues as well. For example, as an AI-based device takes in more information and offers new insights to clinicians, should it be considered a “new” device? Should it go through FDA’s marketing clearance procedures every time it learns something new? And how can the healthcare community trust that the device will make better choices or recommendations a year from now, or five years from now, than it does at its introduction?
Continuous learning The old rules of the road for medical device regulation – which have been around since the 1970s – don’t apply anymore, says Zach Rothstein, vice president, technology and regulatory affairs, AdvaMed. “In terms of regulation, the most unique aspect of AI, or machine learning, is that it can continuously learn,” he points out. “The inputs it receives in the field inform future outputs. The question is, ‘How do you truly allow for that continuous learning aspect of the device to occur?’” Thus far, the FDA has handled the question by granting marketing clearance for AI-based products that are essentially “locked,” says Rothstein. Their algorithms are typically based on thousands of data points – which make them very smart indeed. But they haven’t been FDAcleared to get any “smarter” in the field. In other words, they are prevented from continuously learning. FDA is trying to re-imagine its approach to AIbased devices by adopting a “change management protocol,” which would establish parameters that would allow devices to continuously learn in the field. “Without that, things have to be locked,” says Rothstein. “If a developer wants to update the software of an AI device based on input received from the real world, the developer has to go back to the FDA for marketing clearance.”
FDA trying to catch up To catch up to AI technology, FDA is simultaneously exploring two paths: 1. Precertifying developers of AI-based devices. 2. D eveloping a framework for AI-based medical devices In July 2017, the agency launched its “Pre-cert pilot program” as part of its “Digital Health Innovation Action
Plan.” The gist is to look at the software developer or digital health technology developer, rather than primarily at the product. After reviewing systems for software design, validation and maintenance, FDA would determine whether the company meets quality standards and if so, would precertify the company. The agency compares it to the Transportation Security Administration’s Precheck program, which screens travelers and awards them with a “Known Traveler Number,” speeding up their airport security checks.
FDA is trying to re-imagine its approach to AI-based devices by adopting a ‘change management protocol,’ which would establish parameters that would allow devices to continuously learn in the field. With the information gleaned through the pilot program, the agency hopes to determine the key metrics and performance indicators for precertification and identify ways that precertified companies could potentially submit less information to the FDA than is currently required before marketing a new digital health tool. The FDA is also considering – as part of the pilot program – whether and how precertified companies may not have to submit a product for premarket review in some cases. In September 2017, the agency announced the names of the companies selected to participate in the pilot program The agency’s intention was to include a wide range of companies and technology in the digital health sector, including small startups and large companies, high- and low-risk medical device software products, medical product manufacturers and software developers. Participants selected include:
www.repertoiremag.com
•
October 2019
43