Large Language Models with Vision on Diagnostic Radiology Board Exam Style Questions.

Journal: Academic radiology
PMID: 39632215

Abstract

RATIONALE AND OBJECTIVES: The expansion of large language models to process images offers new avenues for application in radiology. This study aims to assess the multimodal capabilities of contemporary large language models, which allow analysis of image inputs in addition to textual data, on radiology board-style examination questions with images.

Authors

  • Shawn H Sun
    Departments of Radiological Sciences and Computer Science, University of California, Irvine, CA, USA.
  • Kasha Chen
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Samuel Anavim
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Michael Phillipi
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Leslie Yeh
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Kenneth Huynh
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Gillean Cortes
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Julia Tran
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Mark Tran
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Vahid Yaghmai
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.
  • Roozbeh Houshyar
    University of California Irvine, Radiology Department, UCI Medical Center, Orange, California, USA.

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