Performance of GPT-4 Turbo and GPT-4o in Korean Society of Radiology In-Training Examinations.

Journal: Korean journal of radiology
Published Date: Apr 17, 2025

Abstract

OBJECTIVE: Despite the potential of large language models for radiology training, their ability to handle image-based radiological questions remains poorly understood. This study aimed to evaluate the performance of the GPT-4 Turbo and GPT-4o in radiology resident examinations, to analyze differences across question types, and to compare their results with those of residents at different levels.

Authors

  • Arum Choi
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Hyun Gi Kim
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. catharina@catholic.ac.kr.
  • Moon Hyung Choi
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.
  • Shakthi Kumaran Ramasamy
    Department of Radiology, Molecular Imaging Program at Stanford, Stanford University School of Medicine, Stanford, CA, USA.
  • Youme Kim
    Department of Diagnostic Radiology, Dankook University Hospital, Cheonan, Republic of Korea.
  • Seung Eun Jung
    Department of Radiology, Eunpyeong St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

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