Summary of Medqa-cs: Benchmarking Large Language Models Clinical Skills Using An Ai-sce Framework, by Zonghai Yao et al.
MedQA-CS: Benchmarking Large Language Models Clinical Skills Using an AI-SCE Framework
by Zonghai Yao, Zihao Zhang, Chaolong Tang, Xingyu Bian, Youxia Zhao, Zhichao Yang, Junda Wang, Huixue Zhou, Won Seok Jang, Feiyun Ouyang, Hong Yu
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Artificial intelligence (AI) and large language models (LLMs) are crucial in healthcare, requiring advanced clinical skills. Current benchmarks lack comprehensiveness to evaluate these skills effectively. We introduce MedQA-CS, a novel AI-SCE framework inspired by medical education’s Objective Structured Clinical Examinations (OSCEs), designed to address this gap. MedQA-CS assesses LLMs through two instruction-following tasks: LLM-as-medical-student and LLM-as-CS-examiner, mirroring real clinical scenarios. Our contributions include developing a comprehensive evaluation framework with publicly available data and expert annotations. We also provide a quantitative and qualitative assessment of LLMs as reliable judges in CS evaluation. Experimental results demonstrate that MedQA-CS is more challenging than traditional multiple-choice QA benchmarks (e.g., MedQA) for evaluating clinical skills. Combined with existing benchmarks, MedQA-CS enables comprehensive evaluation of LLMs’ clinical capabilities for both open- and closed-source LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper focuses on making artificial intelligence and language models better at understanding medical concepts. Right now, there are no good ways to test how well these AI systems can think like doctors or medical students. The authors introduce a new way to evaluate these AI systems called MedQA-CS. It’s based on real-life clinical scenarios and has two parts: one where the AI system acts as a student and another where it acts as an examiner. The results show that this new method is more challenging than current ways of testing AI systems, which means it can help make them better at understanding medical concepts. |
