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Understanding Bias in Artificial Intelligence: A Practice Perspective.
Davis, Melissa A; Wu, Ona; Ikuta, Ichiro; Jordan, John E; Johnson, Michele H; Quigley, Edward.
Afiliação
  • Davis MA; From Yale University (M.A.D., M.H.J.), New Haven, Connecticut Melissa.a.davis@yale.edu.
  • Wu O; Massachusetts General Hospital (O.W.), Charlestown, Massachusetts.
  • Ikuta I; Mayo Clinic Arizona, Department of Radiology (I.I.), Phoenix, Arizona.
  • Jordan JE; Stanford University School of Medicine (J.E.J.), Stanford, California.
  • Johnson MH; From Yale University (M.A.D., M.H.J.), New Haven, Connecticut.
  • Quigley E; University of Utah (E.Q.), Salt Lake City, Utah.
AJNR Am J Neuroradiol ; 45(4): 371-373, 2024 04 08.
Article em En | MEDLINE | ID: mdl-38123951
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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Radiologia / Inteligência Artificial Idioma: En Ano de publicação: 2024 Tipo de documento: Article