How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 2

Increased transparency is a cornerstone of the recent draft guidance from the Food and Drug Administration (FDA) on submissions for AI-enabled software. While this will result in an increased amount of documentation on the training as well as limitations for adjunctive use of AI models, Nina Kottler, M.D., maintained in a recent interview that this well-intended effort at transparency may fall short of having an impact in daily practice.

“ … Any of this documentation that the FDA is requiring right now is not so helpful at the point of care. It is good that they're doing it. It's not a bad thing. It's a very good thing, but we need a way now to translate that information to be helpful at the point of care,” posited Dr. Kottler, the associate chief medical officer of clinical AI at Radiology Partners.

Whether it comes in the form of heat mapping or a color-coded assessment by the adjunctive AI, Dr. Kottler says the capability of the AI tool to express a degree of confidence in the evaluation can be beneficial for end-user radiologists.

Additionally, Dr. Kottler maintained that there should be clear transparency on how the adjunctive AI is making its assessment. She cautioned that most people don’t recognize that adjunctive AI doesn’t consider every single piece of input data in its interpretation.

“ … Another thing that the FDA should be requiring is to tell the end user, what are the images, or what are the components of the language, or whatever that the AI is using so radiologists know when they're looking at a study, ‘Oh, this series wasn't even evaluated by the AI.’ That is really important information,” emphasized Dr. Kottler.

(Editor’s note: For related content, see “How Will the New FDA Guidance Affect AI Software in Radiology?: An Interview with Nina Kottler, MD, Part 1,” “Mammography Study Shows Merits of AI for Improving Breast Cancer Detection and Effectiveness of Recalls” and “Study Assesses Lung CT-Based AI Models for Predicting Interstitial Lung Abnormality.”)

For more insights from Dr. Kottler, watch the video below.