Benchmarking large language models on persian surgical subspecialty board examinations: a comparative study of ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash

Abstract

This study evaluated the performance of three large language models, including ChatGPT-4o, ChatGPT-5, and Gemini 2.5 Flash, on 532 Persian multiple-choice questions from the 2025 Iranian surgical subspecialty board examinations. Questions spanned five domains: Pediatric, Cardiovascular, Vascular and Endovascular, Thoracic, and Plastic & Reconstructive Surgery. Using standardized prompts, we assessed overall accuracy, variation across subspecialties and question types, and the effect of question length. Gemini 2.5 Flash and ChatGPT-5 achieved higher accuracy (73.9% and 73.3%) than ChatGPT-4o (68.2%). Agreement with the official key was substantial for Gemini 2.5 Flash (κ = 0.651) and ChatGPT-5 (κ = 0.642), and moderate to substantial for ChatGPT-4o (κ = 0.575). Model performance was stable across subspecialties, but all three showed lower accuracy on surgical technique questions compared with clinical scenarios or basic science items. Question length did not affect ChatGPT-5 or Gemini 2.5 Flash, while longer stems reduced ChatGPT-4o's performance. These findings indicate that newer LLMs provide measurable improvements in surgical question answering, though lower performance on surgical technique items supports cautious integration and further multimodal development.

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