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home / papers / 2026 / MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills

MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills

Paper · 2026 · Zonghai Yao, Zihao Zhang, Chaolong Tang et al.

Summary

Most medical LLM benchmarks, such as MedQA, rely on multiple-choice questions. These test what a model knows but not whether it can carry out a patient encounter. MedQA-CS fills this gap with an AI-SCE benchmark modeled on the OSCE format of the USMLE Step 2 CS exam, targeting the "shows how" tier of Miller's Pyramid.

The authors converted 44 publicly available Step 2 CS cases into instruction-following data covering four stages:

  • information gathering
  • physical examination
  • closure
  • differential diagnosis

The texts were paraphrased with ChatGPT and then checked by licensed medical experts. The benchmark has two roles:

  • MedStuLLM: the model acts as the medical student.
  • MedExamLLM: the model grades student performance, and its scores are compared with expert ratings.

Results show that models scoring at or above expert level on MCQ benchmarks perform much worse on MedQA-CS. Separately, carefully designed, guideline-aligned judge prompts reach strong agreement with clinicians. This matters for two reasons: it shows that factual knowledge does not imply clinical skill, and it supports LLM-as-Judge as a workable metric in expert domains.

Key points

  • Dataset size: 1,667 instruction-based data points built from 44 USMLE Step 2 CS cases. For comparison, the real exam decides pass/fail from 12 cases per student.
  • Availability: released on GitHub (bio-nlp/MedQA-CS) and Hugging Face (MedQA-CS-Student, MedQA-CS-Exam) under CC BY-NC 4.0, together with prompts, code and expert annotations.
  • Why not standard metrics: ROUGE and BERTSCORE are judged unsuitable for this open-ended task, so the benchmark relies on LLM-as-Judge, validated against three medical experts.
  • Coverage limits: treatment plans are excluded, because Step 2 CS stops at diagnosis.
  • Format limits: the data is English-only and text-only, with no speech, images or other modalities.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓University of Massachusetts AmherstAmherst Center · United States106 works · 66,692 citations🎓Emory UniversityAtlanta · United States36 works · 39,779 citations🎓University of Massachusetts LowellLowell · United States15 works · 3,942 citations🎓University of MinnesotaMinneapolis · United States28 works · 2,184 citations🎓University of Massachusetts Chan Medical SchoolWorcester · United States2 works · 27 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksComputer Vision and Pattern Recognition · 2,869 worksMedicine · 241 worksHealth Informatics · 184 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1,220 works

Cite

Cite as Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang and Hong Yu. 2026. MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eacl-long.292

@inproceedings{yao2026medqa,
  title = {{MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skills}},
  author = {Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang and Hong Yu},
  booktitle = {Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.eacl-long.292},
  url = {https://doi.org/10.18653/v1/2026.eacl-long.292}
}

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