MedXpertQA: Benchmarking Expert-Level Medical Reasoning and
  Understanding
Authors: name: Yuxin Zuo, name: Shang Qu, name: Yifei Li, name: Zhangren Chen, name: Xuekai Zhu, name: Ermo Hua, name: Kaiyan Zhang, name: Ning Ding, name: Bowen Zhou

We introduce MedXpertQA, a highly challenging and comprehensive benchmark to
evaluate expert-level medical knowledge and advanced reasoning. MedXpertQA
includes 4,460 questions spanning 17 specialties and 11 body systems. It
includes two subsets, Text for text evaluation and MM for multimodal
evaluation. Notably, MM introduces expert-level exam questions with diverse
images and rich clinical information, including patient records and examination
results, setting it apart from traditional medical multimodal benchmarks with
simple QA pairs generated from image captions. MedXpertQA applies rigorous
filtering and augmentation to address the insufficient difficulty of existing
benchmarks like MedQA, and incorporates specialty board questions to improve
clinical relevance and comprehensiveness. We perform data synthesis to mitigate
data leakage risk and conduct multiple rounds of expert reviews to ensure
accuracy and reliability. We evaluate 16 leading models on MedXpertQA.
Moreover, medicine is deeply connected to real-world decision-making, providing
a rich and representative setting for assessing reasoning abilities beyond
mathematics and code. To this end, we develop a reasoning-oriented subset to
facilitate the assessment of o1-like models.