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arXiv · 2405.07348

MedConceptsQA: Open Source Medical Concepts QA Benchmark

Ofir Ben Shoham·Nadav Rappoport
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Abstract

We present MedConceptsQA, a dedicated open source benchmark for medical concepts question answering. The benchmark comprises of questions of various medical concepts across different vocabularies: diagnoses, procedures, and drugs. The questions are categorized into three levels of difficulty: easy, medium, and hard. We conducted evaluations of the benchmark using various Large Language Models. Our findings show that pre-trained clinical Large Language Models achieved accuracy levels close to random guessing on this benchmark, despite being pre-trained on medical data. However, GPT-4 achieves an absolute average improvement of nearly 27%-37% (27% for zero-shot learning and 37% for few-shot learning) when compared to clinical Large Language Models. Our benchmark serves as a valuable resource for evaluating the understanding and reasoning of medical concepts by Large Language Models. Our benchmark is available at https://huggingface.co/datasets/ofir408/MedConceptsQA

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Ofir Ben Shoham, Nadav Rappoport. 2024-05-14. MedConceptsQA: Open Source Medical Concepts QA Benchmark. https://arxiv.org/abs/2405.07348

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