Health AI Central
Peer-Reviewed Publication
medRxiv2025July 21, 2025Journal Article

DREAM: A framework for discovering mechanisms underlying AI prediction of protected attributes.

Soham U Gadgil1, Alex J DeGrave1,2, Joseph D Janizek3, Sonnet Xu4, Lotanna Nwandu5, Fonette Fonjungo6, Su-In Lee1, Roxana Daneshjou6,7
1Paul G. Allen School of Computer Science and Engineering, University of Washington.
2Medical Scientist Training Program, University of Washington.
3Department of Radiology, Stanford University.
4Department of Computer Science, Stanford University.
5OSF Healthcare, College of Medicine, University of Illinois.
6Department of Dermatology, Stanford School of Medicine.
7Department of Biomedical Data Science, Stanford School of Medicine.
PubMed PMC DOI PDF

Abstract

Recent advances in Artificial Intelligence (AI) have started disrupting the healthcare industry, especially medical imaging, and AI devices are increasingly being deployed into clinical practice. Such classifiers have previously demonstrated the ability to discern a range of protected demographic attributes (like race, age, sex) from medical images with unexpectedly high performance, a sensitive t…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free AccountSign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.