arxiv: 2501.18362 · v3 · submitted 2025-01-30 · 💻 cs.AI · cs.CL· cs.CV· cs.LG

Recognition: 2 theorem links

· Lean Theorem

MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding

Yuxin Zuo , Shang Qu , Yifei Li , Zhangren Chen , Xuekai Zhu , Ermo Hua , Kaiyan Zhang , Ning Ding

show 1 more author

Bowen Zhou

Authors on Pith no claims yet

Pith reviewed 2026-05-16 14:22 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.CVcs.LG

keywords MedXpertQAmedical reasoningexpert-level benchmarkmultimodal medical QAclinical knowledge evaluationdata leakage mitigationspecialty board questionsAI model assessment

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The pith

MedXpertQA supplies 4,460 expert-reviewed medical questions across 17 specialties to test genuine clinical reasoning in AI systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper creates MedXpertQA, a benchmark with 4,460 questions in 17 medical specialties to measure how well AI systems handle expert-level medical knowledge and reasoning. It features both text-only and multimodal questions that include actual clinical images, patient records, and exam results, unlike simpler existing tests. The authors applied strict filtering, added new questions from specialty boards, synthesized data to reduce leakage risks, and had experts review everything multiple times. They tested 18 top models to show the benchmark's value. A sympathetic reader would care because better tests in medicine could reveal whether AI can truly support complex real-world decisions rather than just recall facts.

Core claim

MedXpertQA is a benchmark consisting of 4,460 questions spanning 17 specialties and 11 body systems, with a Text subset for textual evaluation and an MM subset for multimodal evaluation featuring diverse images and rich clinical information such as patient records. The benchmark was created through rigorous filtering, data synthesis to mitigate leakage, augmentation, and multiple rounds of expert reviews to ensure accuracy, providing a comprehensive tool for evaluating expert-level medical reasoning.

What carries the argument

The MedXpertQA benchmark, built through expert-reviewed filtering of specialty board questions, data augmentation, leakage mitigation via synthesis, and inclusion of multimodal clinical records and images.

If this is right

Top models can be ranked more reliably by their ability to perform advanced medical reasoning rather than pattern matching.
The multimodal subset tests whether systems can integrate visual clinical data with textual patient records in one workflow.
A dedicated reasoning-oriented subset allows targeted assessment of step-by-step thinking in models like o1.
Medicine becomes a stronger test domain for general reasoning capabilities beyond mathematics and programming.
Developers gain clearer signals on where current AI still falls short of expert clinical decision-making.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

If models succeed here, they may become more trustworthy for assisting physicians, though separate real-world trials would still be required.
The expert-review and synthesis process could transfer to creating rigorous benchmarks in other professional fields such as law or engineering.
Large performance gaps that persist across models would point to architectural limits in handling integrated clinical evidence.

Load-bearing premise

The selected and augmented questions, after expert review and synthesis, accurately represent genuine expert-level clinical reasoning without residual data leakage or selection bias that would inflate model performance.

What would settle it

A model that scores high on MedXpertQA but produces incorrect diagnoses or treatment plans on newly written, never-published clinical cases presented directly by practicing physicians would show the benchmark fails to capture real expert reasoning.

read the original abstract

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 18 leading models on \benchmark. 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. Code and data are available at: https://github.com/TsinghuaC3I/MedXpertQA

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

MedXpertQA gives a larger, more clinically grounded multimodal benchmark plus a reasoning subset, but the leakage controls stay qualitative.

read the letter

MedXpertQA is mainly a new collection of 4,460 questions drawn from 17 specialties and 11 body systems, split into a text track and a multimodal track that supplies full patient records and varied images rather than caption-only pairs. It also carves out a reasoning-oriented subset meant to test models that do chain-of-thought or o1-style search. The authors started from real specialty board material, applied filtering and augmentation to raise difficulty above older sets like MedQA, ran multiple expert review rounds, and released the data and code. They ran 18 models through it, which is useful for seeing where current systems still fall short on expert-level material. That combination of scale, richer clinical context, and the reasoning slice is the concrete addition. The construction steps are described clearly enough that a reader can see the intent to reduce leakage and selection bias. The main limitation is that the leakage mitigation is described only at the level of “data synthesis and expert review.” No n-gram overlap numbers, embedding similarity thresholds, or membership-inference results are given, so it remains possible that some performance still reflects residual exposure to board-exam sources or pretraining data. The multimodal questions look better than prior caption-derived sets, but the same gap in quantitative checks applies. This work is aimed at groups building or auditing medical reasoning systems, especially those who need a harder yardstick than MedQA. Readers who care about benchmark construction details and public artifacts will get direct value from the release. The paper shows enough new material and transparent methodology to deserve a serious referee, though the review should press for the missing leakage metrics. I would send it to peer review.

Referee Report

1 major / 2 minor

Summary. The paper introduces MedXpertQA, a benchmark of 4,460 questions spanning 17 specialties and 11 body systems, split into text-only and multimodal (MM) subsets. It claims to improve on prior datasets like MedQA via rigorous filtering, augmentation, data synthesis for leakage mitigation, and multiple rounds of expert review, while evaluating 18 models and providing a reasoning-oriented subset for o1-like systems.

Significance. If the leakage-mitigation and expert-validation steps hold, MedXpertQA would offer a meaningfully harder and more clinically grounded testbed than existing medical QA collections, particularly through its MM subset that pairs diverse images with rich patient records rather than caption-derived pairs. The public release of code and data is a clear strength for reproducibility.

major comments (1)

[Benchmark Construction / Data Synthesis] The data-synthesis procedure (described in the methods section on benchmark construction) is presented as sufficient to eliminate leakage from specialty-board sources, yet no quantitative audit—n-gram overlap, embedding cosine thresholds, or membership-inference results—is reported between the original questions and the final synthesized set or against common pre-training corpora. This omission directly weakens the central claim that high model scores reflect genuine expert reasoning rather than residual memorization.

minor comments (2)

[Experiments] Table 1 (or the equivalent model-evaluation table) should report per-specialty breakdowns or at least variance across the 17 specialties to support the claim of comprehensive coverage.
[Dataset Description] The abstract states that MM questions contain 'rich clinical information, including patient records and examination results,' but the main text would benefit from one or two concrete examples showing how these elements are formatted and how they differ from prior multimodal medical benchmarks.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive review and for recognizing the potential value of MedXpertQA as a more challenging and clinically grounded benchmark. We address the single major comment below and will revise the manuscript to incorporate additional quantitative validation.

read point-by-point responses

Referee: [Benchmark Construction / Data Synthesis] The data-synthesis procedure (described in the methods section on benchmark construction) is presented as sufficient to eliminate leakage from specialty-board sources, yet no quantitative audit—n-gram overlap, embedding cosine thresholds, or membership-inference results—is reported between the original questions and the final synthesized set or against common pre-training corpora. This omission directly weakens the central claim that high model scores reflect genuine expert reasoning rather than residual memorization.

Authors: We acknowledge that the manuscript describes the data-synthesis steps (filtering, augmentation, and expert review) but does not report quantitative leakage audits such as n-gram overlap statistics, embedding cosine similarities, or membership-inference tests. Our defense rests on the multi-stage process: source questions were drawn from specialty-board exams, heavily rewritten and augmented with new clinical details, then subjected to three rounds of independent expert review to ensure substantive differences. Nevertheless, to directly address the referee's concern and strengthen the central claim, we will add a dedicated subsection in the revised Methods and Appendix that reports (1) average n-gram overlap (1- to 5-grams) between original and synthesized questions, (2) mean and maximum cosine similarities using sentence embeddings, and (3) a comparison against a sample of common pre-training corpora. These metrics will be computed on the released dataset and code, allowing readers to verify the effectiveness of leakage mitigation. revision: yes

Circularity Check

0 steps flagged

Benchmark assembled from external exam sources with independent expert validation

full rationale

The paper constructs MedXpertQA by sourcing questions from external medical board exams and textbooks, then applies filtering, augmentation, data synthesis, and multiple rounds of expert review to create the final 4,460-question set. No equations, predictions, or first-principles derivations are presented that reduce to fitted parameters, self-definitions, or self-citation chains. The central claims rest on the independence of the source materials and the external validation process rather than any internal reduction, keeping the methodology self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard dataset-construction practices plus domain-specific expert validation; no free parameters or invented entities are introduced.

axioms (1)

domain assumption Multiple rounds of expert review ensure accuracy and clinical relevance of questions
Stated in the abstract as part of the construction process.

pith-pipeline@v0.9.0 · 5538 in / 1189 out tokens · 35221 ms · 2026-05-16T14:22:43.284471+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

30 extracted references · 30 canonical work pages · cited by 17 Pith papers · 1 internal anchor

[1]

org/CorpusID:268232499

URL https://api.semanticscholar. org/CorpusID:268232499. Ben Abacha, A., Hasan, S. A., Datla, V . V ., Demner- Fushman, D., and M ¨uller, H. Vqa-med: Overview of the medical visual question answering task at imageclef

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HuatuoGPT-o1, Towards Medical Complex Reasoning with LLMs

In Proceedings of CLEF (Conference and Labs of the Evaluation Forum) 2019 Working Notes. 9-12 Septem- ber 2019, 2019. Cai, Z., Cao, M., Chen, H., Chen, K., Chen, K., Chen, X., Chen, X., Chen, Z., Chen, Z., Chu, P., Dong, X., Duan, H., Fan, Q., Fei, Z., Gao, Y ., Ge, J., Gu, C., Gu, Y ., Gui, T., Guo, A., Guo, Q., He, C., Hu, Y ., Huang, T., Jiang, T., Jia...

work page internal anchor Pith review Pith/arXiv arXiv 2019
[3]

The reduction was successful, as indicated by follow-up x-rays

Injury and Reduction: The man had a shoulder injury, likely a dislocation, based on initial x-rays and description. The reduction was successful, as indicated by follow-up x-rays

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[4]

Symptoms Post-Reduction: After 10 days, the patient demonstrates an inability to abduct the shoulder

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[5]

Common Cause of Inability to Abduct: Injury to the axillary nerve can cause an inability to abduct the shoulder, as it innervates the deltoid muscle

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[6]

If there is a suspicion of nerve injury, such as the axillary nerve in this case, an EMG would be helpful in confirming nerve dysfunction or damage

Most Appropriate Diagnostic Test: Electromyography (EMG) is often used to assess the function of muscles and nerves. If there is a suspicion of nerve injury, such as the axillary nerve in this case, an EMG would be helpful in confirming nerve dysfunction or damage. Considering the options: (A) CT-angiogram of the affected extremity would assess vascular i...

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[7]

Heart Rate: The patient has a heart rate of 140 beats per minute, which indicates tachycardia

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[8]

There are no distinct P waves visible before each QRS complex; instead, there is a disorganized electrical activity, which is typical of fibrillatory waves

ECG Analysis: The ECG shows an irregularly irregular rhythm. There are no distinct P waves visible before each QRS complex; instead, there is a disorganized electrical activity, which is typical of fibrillatory waves

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[9]

saw-tooth

Differential Diagnosis: (A) Atrial flutter: Typically gives a “saw-tooth” pattern and usually has a more regular rhythm. (B) Multifocal atrial tachycardia (MAT): Characterized by an irregular rhythm with at least three different P wave morphologies which isn’t apparent here. (C) Atrial fibrillation with rapid ventricular response: Characterized by an irre...

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[10]

saw-tooth

Post-Surgical Context: Atrial fibrillation (AF) is relatively common after cardiac surgery, such as coronary artery bypass grafting, due to inflammation, electrolyte imbalances, and other stressors on the heart. Considering the irregularly irregular rhythm and absence of distinct P waves, the most probable diagnosis is: (C) Atrial fibrillation with rapid ...

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[11]

Analysis: The above response fails to fully grasp the question’s implications in several ways

They also mask typical warning signs of hypoglycemia (e.g., palpitations or tremors), which may lead to unrecognized or prolonged hypoglycemic episodes. Analysis: The above response fails to fully grasp the question’s implications in several ways. For the incorrect answer (G) Atenolol, the model generally states that some beta-blockers reduce glycogenolys...

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[12]

Aging is associated with alterations in sleep architecture, which may lead to reductions in total sleep duration, slow-wave (deep) sleep, and sleep efficiency

Age-related sleep changes: The patient is 69 years old. Aging is associated with alterations in sleep architecture, which may lead to reductions in total sleep duration, slow-wave (deep) sleep, and sleep efficiency. Older adults often feel less refreshed upon waking, even when sleep appears otherwise normal

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[13]

His sleep appears peaceful, and he experiences no disruptive symptoms such as snoring or awakenings

Patient’s sleep specifics: He now sleeps 7 hours and wakes feeling less refreshed compared to his younger years when sleeping 9 hours. His sleep appears peaceful, and he experiences no disruptive symptoms such as snoring or awakenings

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[14]

Ruling out other causes: Good management of hypertension and diabetes, normal vitals, a well-rounded active lifestyle, and no reported fatigue or significant tiredness suggest no acute medical condition contributing to his symptoms

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[15]

This can result in less restorative sleep and a less refreshed feeling upon waking

Physiological changes associated with aging: Aging often leads to a decrease in growth hormone, which is secreted primarily during slow-wave sleep and stimulates restorative processes during sleep. This can result in less restorative sleep and a less refreshed feeling upon waking

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[16]

acute on chronic

Other options to consider: Decreased melatonin (option F): Melatonin levels decline with age, but melatonin primarily regulates sleep initiation and circadian rhythm rather than sleep quality or restoration. Altered circadian rhythm proteins (option D): Circadian changes occur with aging, but his symptoms are more closely linked to sleep restoration rathe...

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[17]

**Language Rewriting**: Use different expressions and sentence structures that are equivalent in meaning to the original.,→

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[18]

**Information Restructuring**: Alter the order that information is presented in the original question without disrupting the logical coherence of the question.,→ ### General Requirements

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[19]

**Maintain the core content:** Ensure that the rephrased question retains the core content, reasoning logic, and correct answer of the original question.,→

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[20]

Rigorously ensure clarity and avoid ambiguity

**Ensure a professional language style:** Maintain a professional, formal, and clear language style similar to the original question. Rigorously ensure clarity and avoid ambiguity. Feel free to copy parts of the original question if alternative appropriate phrasing is not possible. ,→ ,→ ,→

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[21]

Do not change, add, or delete any factual information

**Maintain factual consistency:** Ensure that the rewritten question retains every piece of information in the original. Do not change, add, or delete any factual information. ,→ ,→

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[22]

Pay special attention to keep any tabular data in completely the same format as the original

**Imitate original formatting:** Keep any special formatting in the original question unchanged, especially regarding structured data presentation. Pay special attention to keep any tabular data in completely the same format as the original. ,→ ,→

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[23]

Answer Choices: (A) [Option A] (B) [Option B]

**Final output format:** Ensure that the options section of the question remains unchanged and the format remains as "Answer Choices: (A) [Option A] (B) [Option B] ...". Only output the rephrased question. Do not include any additional information or explanations. ,→ ,→ ,→ {demonstrations} ### Input **Original Question:** {question} **Correct Answer:** {l...

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[24]

**Consider Errorneous Perspectives:** Include distractors that interpret key information in the question incorrectly.,→

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[25]

**Leverage Common Misconceptions:** Consider designing distractors leveraging common errors or medical concepts that are frequently confused.,→

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[26]

**Logical Misdirection:** Introduce distractors grounded in logical reasoning that is seemingly plausible but incorrect.,→ ### General Requirements

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[27]

They should be clear, concise, and professionally worded

**Maintain Consistency:** Ensure that the generated new options match the original options in terms of length, structure, word count, and grammatical form. They should be clear, concise, and professionally worded. ,→ ,→

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[28]

**Avoid Oversimplified Distractors:** Do not include options that can be easily dismissed based on intuition or surface-level analysis.,→

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[29]

Avoid options that are overtly illogical or unsupported.,→

**Ensure High Plausibility:** Maintain the plausibility of each generated option. Avoid options that are overtly illogical or unsupported.,→

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[30]

Answer Choices: (A) [Option A] (B) [Option B]

**Final Format:** Present the original question and options, followed by the **{generate_num}** additional options. Ensure that the generated options follow the same format as the original: "Answer Choices: (A) [Option A] (B) [Option B] ...". Do not output anything after the options. ,→ ,→ ,→ {demonstrations} ### Input **Original Question:** {question} **...

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