Artificial Intelligence in Radiology: A Leadership Survey.

Journal: Journal of the American College of Radiology : JACR
PMID: 39800091

Abstract

PURPOSE: Surveys to assess views about artificial intelligence (AI) of various diagnostic radiology constituencies have revealed interesting combinations of enthusiasm, caution, and implementation priorities. We surveyed academic radiology leaders about their views on AI and how they intend to approach AI implementation in their departments.

Authors

  • Elizabeth S Burnside
    Department of Radiology, University of Wisconsin, Madison, WI, United States.
  • Thomas M Grist
    Departments of Radiology, Medical Physics, and Biomedical Engineering, The University of Wisconsin-Madison School of Medicine and Public Health, Madison, WI, USA.
  • Michael R Lasarev
    University of Wisconsin, Madison, Wisconsin.
  • John W Garrett
    From the Departments of Medical Physics (R.Z., X.T., C.Z., D.G., J.W.G., K.L., S.B.R., G.H.C.) and Radiology (M.L.S., J.W.G., K.L., S.B.R., G.H.C.), University of Wisconsin-Madison School of Medicine and Public Health, 1111 Highland Ave, Madison, WI 53705; and Department of Radiology, Henry Ford Health System, Detroit, Mich (Z.Q., N.B.B., T.K.S., J.D.N,).
  • Elizabeth A Morris
    Department of Radiology, Breast Imaging Service, Memorial Sloan Kettering Cancer Center, New York, NY.

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