@inproceedings{yao-etal-2026-medqa,
    title = "{M}ed{QA}-{CS}: Objective Structured Clinical Examination ({OSCE})-Style Benchmark for Evaluating {LLM} Clinical Skills",
    author = "Yao, Zonghai  and
      Zhang, Zihao  and
      Tang, Chaolong  and
      Bian, Xingyu  and
      Zhao, Youxia  and
      Yang, Zhichao  and
      Wang, Junda  and
      Zhou, Huixue  and
      Jang, Won Seok  and
      Ouyang, Feiyun  and
      Yu, Hong",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.292/",
    pages = "6183--6257",
    ISBN = "979-8-89176-380-7",
    abstract = "Artificial intelligence (AI) and large language models (LLMs) in healthcare require advanced clinical skills (CS), yet current benchmarks fail to evaluate these comprehensively. We introduce MedQA-CS, an AI-SCE framework inspired by medical education{'}s Objective Structured Clinical Examinations (OSCEs), to address this gap. MedQA-CS evaluates LLMs through two instruction-following tasks{---}LLM-as-medical-student and LLM-as-CS-examiner{---}designed to reflect real clinical scenarios. Our contributions include developing MedQA-CS, a comprehensive evaluation framework with publicly available data and expert annotations, and providing the quantitative and qualitative assessment of LLMs as reliable judges in CS evaluation. Our experiments show that MedQA-CS is a more challenging benchmark for evaluating clinical skills than traditional multiple-choice QA benchmarks (e.g., MedQA). Combined with existing benchmarks, MedQA-CS enables a more comprehensive evaluation of LLMs' clinical capabilities for both open- and closed-source LLMs."
}