Comparative evaluation of six large language models in transfusion medicine: Addressing language and domain-specific challenges.

Journal: Vox sanguinis
Published Date: May 23, 2025

Abstract

BACKGROUND AND OBJECTIVES: Large language models (LLMs) such as GPT-4 are increasingly utilized in clinical and educational settings; however, their validity in subspecialized domains like transfusion medicine remains insufficiently characterized. This study assessed the performance of six LLMs on transfusion-related questions from Korean national licensing examinations for medical doctors (MDs) and medical technologists (MTs).

Authors

  • Jong Kwon Lee
    Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
  • Sholhui Park
    Department of Laboratory Medicine, Ewha Womans University College of Medicine, Seoul, Korea.
  • Sang-Hyun Hwang
    Department of Laboratory Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
  • Jaejoon Lee
    Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea. jaejoonlee.lee@samsung.com.
  • Duck Cho
    Department of Laboratory Medicine and Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
  • Sooin Choi
    Department of Laboratory Medicine and Genetics, Soonchunhyang University Bucheon Hospital, Soonchunhyang University College of Medicine, Bucheon, Korea.

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