A survey of ASER members on artificial intelligence in emergency radiology: trends, perceptions, and expectations.

Journal: Emergency radiology
Published Date: Mar 13, 2023

Abstract

PURPOSE: There is a growing body of diagnostic performance studies for emergency radiology-related artificial intelligence/machine learning (AI/ML) tools; however, little is known about user preferences, concerns, experiences, expectations, and the degree of penetration of AI tools in emergency radiology. Our aim is to conduct a survey of the current trends, perceptions, and expectations regarding AI among American Society of Emergency Radiology (ASER) members.

Authors

  • Anjali Agrawal
    Teleradiology Solutions, 12B Sriram Road, Civil Lines, Delhi, 110054, India. anjali.agrawal@telradsol.com.
  • Garvit D Khatri
    Department of Radiology, University of Washington School of Medicine, Seattle, WA, USA.
  • Bharti Khurana
    Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA bkhurana@bwh.harvard.edu.
  • Aaron D Sodickson
    Department of Radiology, Division of Emergency Radiology, Brigham and Women's Hospital, Boston, 02115, USA. asodickson@bwh.harvard.edu.
  • Yuanyuan Liang
    Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore.
  • David Dreizin
    From the Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine (D.D.), R Adams Cowley Shock Trauma Center, School of Medicine, University of Maryland; Department of Computer Science (Y.Z.), Center for Cognition Vision and Learning, Johns Hopkins University; Diagnostic Radiology and Nuclear Medicine (T.C., G.L.), University of Maryland School of Medicine; Department of Computer Science (A.L.Y.), Center for Cognition Vision and Learning, Johns Hopkins University; Vascular Surgery (A.M., J.J.M.), R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, Maryland.

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