MedXpertQA: Benchmarking Expert-Level Medical Reasoning and
  Understanding
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.
679c5b0034f5df4416915177
Yuxin Zuo
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2025-04-23T15:57:12.013Z
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Yuxin Zuo
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yuxinzuo
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Shang Qu
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2025-01-31T08:35:48.269Z
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Shang (Lindsay) Qu
user
lindsay-qu
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Yifei Li
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Zhangren Chen
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Xuekai Zhu
claimed_verified
2025-09-19T06:52:06.762Z
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zhu
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xuekai
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Ermo Hua
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Kaiyan Zhang
claimed_verified
2025-02-11T15:33:58.392Z
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Kaiyan Zhang
user
iseesaw
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Ning Ding
claimed_verified
2025-01-31T09:50:45.999Z
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Ning Ding
user
stingning
679c5b0034f5df441691517f
Bowen Zhou
MedXpertQA: Benchmarking Expert-Level Medical Reasoning and
  Understanding
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.
MedXpertQA is a benchmark for evaluating medical expertise and reasoning with a focus on advanced clinical knowledge and diverse data, including images and patient records.
benchmark MedXpertQA text evaluation multimodal evaluation expert-level questions clinical information data synthesis expert reviews reasoning-oriented subset