Peer-Reviewed Publication
Nat Biomed Eng2025;9(3):294-306.March 1, 2025Journal Article
Auditing the inference processes of medical-image classifiers by leveraging generative AI and the expertise of physicians.
Alex J DeGrave1,2, Zhuo Ran Cai3, Joseph D Janizek1,2, Roxana Daneshjou4,5, Su-In Lee6
1Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
2Medical Scientist Training Program, University of Washington, Seattle, WA, USA.
3Program for Clinical Research and Technology, Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA.
4Department of Dermatology, Stanford University School of Medicine, Stanford, CA, USA. roxanad@stanford.edu.
5Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA. roxanad@stanford.edu.
6Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA. suinlee@cs.washington.edu.
Abstract
The inferences of most machine-learning models powering medical artificial intelligence are difficult to interpret. Here we report a general framework for model auditing that combines insights from medical experts with a highly expressive form of explainable artificial intelligence. Specifically, we leveraged the expertise of dermatologists for the clinical task of differentiating melanomas from m…
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