Towards objective and systematic evaluation of bias in artificial intelligence for medical imaging.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Nov 1, 2024

Abstract

OBJECTIVE: Artificial intelligence (AI) models trained using medical images for clinical tasks often exhibit bias in the form of subgroup performance disparities. However, since not all sources of bias in real-world medical imaging data are easily identifiable, it is challenging to comprehensively assess their impacts. In this article, we introduce an analysis framework for systematically and objectively investigating the impact of biases in medical images on AI models.

Authors

  • Emma A M Stanley
    Biomedical Engineering Graduate Program, University of Calgary, Calgary, Alberta, T2N 1N4, Canada.
  • Raissa Souza
    Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada.
  • Anthony J Winder
    Department of Radiology, University of Calgary, Calgary, Canada.
  • Vedant Gulve
    Department of Radiology, University of Calgary, Calgary, Alberta, T2N 4N1, Canada.
  • Kimberly Amador
    Department of Radiology, University of Calgary, Calgary, AB, Canada.
  • Matthias Wilms
    Department of Radiology, University of Calgary, Calgary, AB, Canada.
  • Nils D Forkert
    Department of Radiology, University of Calgary, Calgary, Canada. nils.forkert@ucalgary.ca.

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