Understanding Bias in Artificial Intelligence: A Practice Perspective.

Journal: AJNR. American journal of neuroradiology
Published Date:

Abstract

In the fall of 2021, several experts in this space delivered a Webinar hosted by the American Society of Neuroradiology (ASNR) Diversity and Inclusion Committee, focused on expanding the understanding of bias in artificial intelligence, with a health equity lens, and provided key concepts for neuroradiologists to approach the evaluation of these tools. In this perspective, we distill key parts of this discussion, including understanding why this topic is important to neuroradiologists and lending insight on how neuroradiologists can develop a framework to assess health equity-related bias in artificial intelligence tools. In addition, we provide examples of clinical workflow implementation of these tools so that we can begin to see how artificial intelligence tools will impact discourse on equitable radiologic care. As continuous learners, we must be engaged in new and rapidly evolving technologies that emerge in our field. The Diversity and Inclusion Committee of the ASNR has addressed this subject matter through its programming content revolving around health equity in neuroradiologic advances.

Authors

  • Melissa A Davis
    Department of Radiology and Biomedical Imaging, Yale University School of Medicine, 330 Cedar St. Tompkins East TE-2, New Haven, CT 06520.
  • Ona Wu
  • Ichiro Ikuta
  • John E Jordan
    Stanford University School of Medicine (J.E.J.), Stanford, California.
  • Michele H Johnson
    Department of Radiology and Biomedical Imaging, Yale School of Medicine, Box 208042, Tompkins East 2, 333 Cedar St, New Haven, CT 06520-8042, United States of America. Electronic address: michele.h.johnson@yale.edu.
  • Edward Quigley
    University of Utah (E.Q.), Salt Lake City, Utah.