ArticleKorean journal of radiology2025
Performance of GPT-4 Turbo and GPT-4o in Korean Society of Radiology In-Training Examinations.
Article in Korean journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Reporting Quality of Large Language Model Studies: A Cross-Sectional Audit of High-Ranking Radiology and Medical Imaging Journals.Korean journal of radiology · 2026Article
- Evaluating guideline adherence in LLM studies using LLMs.Japanese journal of radiology · 2026Article
- Do General-Purpose Multimodal Large Language Models Really See Radiologic Images or Rely on Text?Korean journal of radiology · 2026Article
- Radiology Research and Publishing Across 2025-2026: Perspectives FromKorean journal of radiology · 2026Article
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Authors and funding
6 authors.
Funding
Abstract
objectiveDespite 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. MATERIALS AND
methodsA total of 776 multiple-choice questions from the Korean Society of Radiology In-Training Examinations were used, forming two question sets: one originally written in Korean and the other translated into English. We evaluated the performance of GPT-4 Turbo (gpt-4-turbo-2024-04-09) and GPT-4o (gpt-4o-2024-11-20) on these questions with the temperature set to zero, determining the accuracy based on the majority vote from five independent trials. We analyzed their results using the question type (text-only vs. image-based) and benchmarked them against nationwide radiology residents' performance. The impact of the input language (Korean or English) on model performance was examined.
resultsGPT-4o outperformed GPT-4 Turbo for both image-based (48.2% vs. 41.8%,
conclusionGPT-4o outperformed the GPT-4 Turbo in all question types. On image-based questions, both models' performance matched that of 1st-year residents but was lower than that of higher-year residents. Both models demonstrated superior performance compared to residents for text-only questions. The models showed consistent performances across English and Korean inputs.
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