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May 16, 2026Istanbul Medical Journal0 citationsOpen Access

Performance Evaluation of Large Language Models in Emergency Medicine Specialty Examination Questions: A Cross-Sectional Study

ŞKŞebnem Zeynep Eke KurtSBSuphi Bahadırlı

Key Points

  • This research aims to evaluate the performance of large language models (LLMs) in emergency medicine specialty examinations using the TUS format.
  • Cross-sectional study design comparing LLM performance to traditional examination formats.
  • Analyzed responses to TUS questions in the context of linguistic and curricular differences.
  • Focused on emergency medicine specialty examinations to assess contextual relevance.
  • LLMs demonstrated varying levels of performance when answering TUS questions compared to USMLE-style exams.
  • Performance metrics indicated strengths and weaknesses specific to the emergency medicine context, influencing assessment validity.

Abstract

Unlike prior studies primarily focused on USMLE-style examinations, this study evaluates LLM performance using the TUS, which reflects a different linguistic and curricular context.By directly comparing LLMs

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Cite This Study

Kurt et al. (2026) studied this question.

synapsesocial.com/papers/6a0808afa487c87a6a40af33https://doi.org/10.4274/imj.galenos.2026.04742
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Performance of large language models in medical licensing examinations: a systematic review and meta-analysis2025
  2. 2Evaluating the Performance of Large Language Models on the CONACEM Anesthesiology Certification Exam: A Comparison with Human Participants2025 · 2 citations
  3. 3Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis (Preprint)2024 · 1 citations
  4. 4Performance of Large Language Models on a Neurology Board–Style Examination2023 · 119 citations
  5. 5Performance Evaluation of Large Language Models in Multilingual Medical Multiple-Choice Questions: Mixed Methods Study.2026