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Related Experiment Video

Updated: Jun 12, 2026

Learning Modern Laryngeal Surgery in a Dissection Laboratory

Learning Modern Laryngeal Surgery in a Dissection Laboratory

Published on: March 18, 2020

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

Zonghai Yao¹, Zihao Zhang², Chaolong Tang¹

¹University of Massachusetts, Amherst.

Proceedings of the Conference. Association for Computational Linguistics. European Chapter. Conference

|June 11, 2026

Summary

We developed MedQA-CS, a new benchmark for evaluating artificial intelligence (AI) clinical skills. This AI framework assesses large language models (LLMs) in realistic medical scenarios, offering a more rigorous evaluation than traditional methods.

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Last Updated: Jun 12, 2026

Learning Modern Laryngeal Surgery in a Dissection Laboratory

Learning Modern Laryngeal Surgery in a Dissection Laboratory

Published on: March 18, 2020

Area of Science:

Medical Education
Artificial Intelligence in Healthcare
Clinical Skills Assessment

Background:

Current benchmarks inadequately assess artificial intelligence (AI) and large language models (LLMs) in healthcare, particularly regarding advanced clinical skills (CS).
There is a need for robust evaluation frameworks that mirror real-world clinical complexities to gauge AI's readiness for medical applications.

Purpose of the Study:

To introduce MedQA-CS, a novel AI-SCE framework designed to comprehensively evaluate the clinical skills of LLMs.
To establish a more challenging and realistic benchmark for AI in healthcare, inspired by medical education's Objective Structured Clinical Examinations (OSCEs).

Main Methods:

Developed MedQA-CS, an evaluation framework utilizing two instruction-following tasks: LLM-as-medical-student and LLM-as-CS-examiner.
Created a publicly available dataset with expert annotations for evaluating LLMs in simulated clinical scenarios.
Assessed LLMs' capabilities as both learners and evaluators of clinical skills.

Main Results:

MedQA-CS provides a more challenging benchmark for clinical skills evaluation compared to traditional multiple-choice question-answering benchmarks like MedQA.
Demonstrated the effectiveness of MedQA-CS in quantitatively and qualitatively assessing LLMs' reliability as judges of clinical skills.
Experiments confirmed MedQA-CS's ability to offer a more comprehensive evaluation of LLMs' clinical capabilities.

Conclusions:

MedQA-CS represents a significant advancement in evaluating AI and LLMs for healthcare applications, particularly in assessing clinical skills.
The framework enables a more thorough understanding of LLMs' strengths and weaknesses in realistic clinical contexts.
MedQA-CS, when used with existing benchmarks, facilitates a holistic assessment of both open- and closed-source LLMs' clinical proficiency.