Polish Medical Exams: A new dataset for cross-lingual medical knowledge transfer assessment

• URL: http://arxiv.org/abs/2412.00559v1
• Date: Sat, 30 Nov 2024 19:02:34 GMT
• Title: Polish Medical Exams: A new dataset for cross-lingual medical knowledge transfer assessment
• Authors: Łukasz Grzybowski, Jakub Pokrywka, Michał Ciesiółka, Jeremi I. Kaczmarek, Marek Kubis,
• Abstract summary: This study introduces a novel benchmark dataset based on Polish medical licensing and specialization exams. It comprises over 24,000 exam questions, including a subset of parallel Polish-English corpora. We evaluate state-of-the-art LLMs, including general-purpose, domain-specific, and Polish-specific models, and compare their performance against human medical students.
• Score: 0.865489625605814
• License:
• Abstract: Large Language Models (LLMs) have demonstrated significant potential in handling specialized tasks, including medical problem-solving. However, most studies predominantly focus on English-language contexts. This study introduces a novel benchmark dataset based on Polish medical licensing and specialization exams (LEK, LDEK, PES) taken by medical doctor candidates and practicing doctors pursuing specialization. The dataset was web-scraped from publicly available resources provided by the Medical Examination Center and the Chief Medical Chamber. It comprises over 24,000 exam questions, including a subset of parallel Polish-English corpora, where the English portion was professionally translated by the examination center for foreign candidates. By creating a structured benchmark from these existing exam questions, we systematically evaluate state-of-the-art LLMs, including general-purpose, domain-specific, and Polish-specific models, and compare their performance against human medical students. Our analysis reveals that while models like GPT-4o achieve near-human performance, significant challenges persist in cross-lingual translation and domain-specific understanding. These findings underscore disparities in model performance across languages and medical specialties, highlighting the limitations and ethical considerations of deploying LLMs in clinical practice.

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