Artificial Intelligence for Teaching Case Curation: Evaluating Model Performance on Imaging Report Discrepancies.

Journal: Academic radiology
Published Date: Mar 9, 2025

Abstract

RATIONALE AND OBJECTIVES: Assess the feasibility of using a large language model (LLM) to identify valuable radiology teaching cases through report discrepancy detection.

Authors

  • Michael Bartley
    Department of Radiology, UW-Madison School of Medicine & Public Health, Madison, Wisconsin (M.B., Z.H., X.T., A.B.R., T.K., J.D.W., T.B., E.M.L.).
  • Zachary Huemann
    Department of Radiology, University of Wisconsin-Madison, Madison, WI, 53705, USA. zhuemann@wisc.edu.
  • Junjie Hu
    Department of Toxicology and Sanitary Chemistry, School of Public Health, Capital Medical University, Beijing 100069, PR China; Beijing Key Laboratory of Environmental Toxicology, Capital Medical University, Beijing 100069, PR China.
  • Xin Tie
    The Hong Kong Polytechnic University, Hong Kong SAR, China.
  • Andrew B Ross
    University of Wisconsin School of Medicine and Public Health, 750 Highland Ave, Madison, WI 53705.
  • Tabassum Kennedy
    Department of Radiology, UW-Madison School of Medicine & Public Health, Madison, Wisconsin (M.B., Z.H., X.T., A.B.R., T.K., J.D.W., T.B., E.M.L.).
  • Joshua D Warner
    Department of Radiology, UW-Madison School of Medicine & Public Health, Madison, Wisconsin (M.B., Z.H., X.T., A.B.R., T.K., J.D.W., T.B., E.M.L.).
  • Tyler Bradshaw
  • Edward M Lawrence
    Department of Radiology, University of Wisconsin, Madison, Wisconsin, USA.

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