RT Journal Article
SR Electronic
T1 Understanding Bias in Artificial Intelligence: A Practice Perspective
JF American Journal of Neuroradiology
JO Am. J. Neuroradiol.
FD American Society of Neuroradiology
SP 371
OP 373
DO 10.3174/ajnr.A8070
VO 45
IS 4
A1 Davis, Melissa A.
A1 Wu, Ona
A1 Ikuta, Ichiro
A1 Jordan, John E.
A1 Johnson, Michele H.
A1 Quigley, Edward
YR 2024
UL http://www.ajnr.org/content/45/4/371.abstract
AB SUMMARY: 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.AIartificial intelligenceASNRAmerican Society of NeuroradiologyTATturnaround time