FDA taking the long view of generative AI in healthcare
The FDA’s fledgling Digital Health Advisory Committee (DHAC) only held its first meeting last week, but it has already committed its thinking to writing.
And what’s on its mind, it turns out, is making sure the agency keeps a close watch on medical devices equipped with GenAI all the days of these products’ lives.
That’s clear from a reading of the 30-page document committee members received ahead of their inaugural November meeting. Here are excerpts, organized as responses to some questions AIin.Healthcare would have liked to ask had we been there.
Why is a total product life cycle (TPLC) strategy critical to the oversight of medical devices equipped with GenAI?
FDA’s long-standing commitment to a TPLC approach has become increasingly relevant for medical devices incorporating technologies that are intended to iterate faster and more frequently over a device’s life of use than ever before.
‘A TPLC approach is likely to remain important to the management of future, safe and effective GenAI-enabled medical devices.’
How does FDA’s TPLC approach relate to the agency’s AI Lifecycle template?
In general, consideration of the FDA’s AI Lifecycle for GenAI-enabled devices—and AI-enabled devices broadly—may be one important way for manufacturers to approach managing their devices throughout the TPLC.
‘Additionally, the AI Lifecycle can be used as a helpful model to identify where new techniques, approaches or standards may be needed to assure adequate management of these new technologies across the TPLC.’
Back to the basics for a minute. How is the FDA defining ‘GenAI?’
GenAI refers to the class of AI models that mimic the structure and characteristics of input data to generate derived synthetic content, and can include images, videos, audio, text and digital content.
‘GenAI models can analyze input data and produce contextually appropriate outputs that may not have been explicitly seen in its training data.’
How does GenAI resemble—and differ from—traditional AI/machine learning?
Like other AI/ML models, GenAI models are frequently developed on datasets so large that human developers typically cannot know everything about the dataset contents during development.
‘In contrast to the datasets used to develop other AI/ML models, datasets for GenAI model development can be intentionally broad and may not be initially tailored to a specific task.’
What makes GenAI especially tricky to regulate?
At times, GenAI’s ability to tackle diverse, new and complex tasks may contribute to uncertainty around the limits of a device’s output.
‘When insufficiently controlled, this uncertainty can translate to difficulty in confirming the bounds of a device’s intended use, which can introduce challenges to FDA’s regulation of GenAI-enabled devices.’
Why does it matter that many GenAI models are foundation models?
Foundation models are trained on a wide range of data and can be broadly applied to numerous AI applications for undertaking myriad tasks.
‘If a manufacturer uses a foundation model or other GenAI tool as part of a product with a specific intended use that meets the definition of a medical device, the product that leverages the foundation model may be the focus of FDA’s device regulatory oversight.’
How best to avoid FDA rejection of a GenAI product?
At times, it may be helpful for manufacturers and developers to consider that a GenAI implementation of a product may not be beneficial to public health. This may be the case when the implementation could provide erroneous or false content.
‘It is helpful for manufacturers and developers to consider when GenAI may or may not be the best technology for a specific intended use.’
Going forward, FDA notes, the performance evaluation methodologies needed for sound oversight “will be governed by the specific intended use and design of the GenAI-enabled device, some of which may necessitate formulation of new performance metrics for certain intended uses.”
‘As with all devices, the totality of evidence—which may include premarket and postmarket evidence—can support reasonable assurance of safety and effectiveness of these devices across the TPLC.’
Dave P. has worked in journalism, marketing and public relations for more than 30 years, frequently concentrating on hospitals, healthcare technology and Catholic communications. He has also specialized in fundraising communications, ghostwriting for CEOs of local, national and global charities, nonprofits and foundations.