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DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models

T0 review · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read DiagnosisArena, a 1,113-case benchmark from top journals, shows state-of-the-art LLMs achieve at most 51% top-1 diagnostic accuracy, far below clinical-level competence.

desk verdict A genuinely useful hard-case benchmark for LLM diagnosis, but the 'far from professional-level' headline needs a human baseline it doesn't have. read the letter →

arxiv 2505.14107 v4 pith:XJ5LJAZE submitted 2025-05-20 cs.CL cs.AI

Yakun Zhu , Zhongzhen Huang , Linjie Mu , Yutong Huang , Wei Nie , Jiaji Liu , Shaoting Zhang , Pengfei Liu
show 1 more author
Xiaofan Zhang
This is my paper
classification cs.CLcs.AI
keywords diagnosticreasoningdiagnosisarenamodelsbenchmarkclinicalchallengeslanguage
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

DiagnosisArena is a test for how well large language models can diagnose diseases from real patient cases. The authors collected 4,175 case reports from journals such as the New England Journal of Medicine and the Lancet, then stripped out treatment plans and follow-up details so the models see only the information a doctor would have before making a diagnosis. After several rounds of filtering, including checks by other AI models and by board-certified physicians, 1,113 cases remained. Each case includes the patient's history, physical examination findings, and diagnostic test results, with the published final diagnosis as the answer key.

The authors asked many AI models to list their top five diagnoses for each case. Even the strongest model tested, OpenAI's o3, got the exact diagnosis right only about 51% of the time. Older models such as o1 scored 31% and DeepSeek-R1 scored 18%. When the same cases were turned into multiple-choice questions, model scores jumped sharply; o1 rose from 31% to about 62%. This gap suggests that selecting an answer from a list is much easier than generating the correct diagnosis from scratch.

The benchmark is meant to be a more realistic measure of clinical reasoning than tests based on medical licensing exam questions. However, the paper does not report how well human doctors perform on the same cases, and it relies on another AI model to judge whether an answer is correct. Those are important caveats for interpreting the headline numbers.

Extended reading notes

Core claim

The paper's central claim is that current LLMs, including advanced reasoning models, are far from professional-level diagnostic competence on realistic clinical cases: 'even the most advanced reasoning models, o3, o1, and DeepSeek-R1, achieve only 51.12%, 31.09%, and 17.79% accuracy, respectively' on DiagnosisArena, and 'multiple-choice formats inherently reduce task difficulty and thus fail to accurately reflect the models' true abilities' (Section 4.3). If correct, no current model can be relied upon for autonomous diagnosis on cases with sufficient clinical information to reach a diagnosis.

Load-bearing premise

The GPT-4o judge used to score open-ended outputs reliably reproduces human expert judgments of 'identical', 'relevant', and 'irrelevant' diagnoses. The paper does not validate this judge against human ratings, yet every reported top-1 and top-5 accuracy in Figure 3 depends on it. If the judge is miscalibrated, the headline numbers could be materially wrong. This assumption enters in Section 4.2 (GPT-4o Evaluation Prompt) and the results in Section 4.3.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The benchmark claim rests on hand-set thresholds rather than fitted mathematical parameters. The free parameters shape which cases enter the benchmark and how answers are scored. The GPT-4o judge assumption is the most consequential for the headline accuracies.

free parameters (3)
  • top_k_outputs = 5
    The evaluation prompt asks models to list the top 5 most likely diagnoses; top-1 and top-5 accuracy are defined relative to this hand-set number (Section 4.2, DiagnosisArena Evaluation Prompt).
  • iterative_filtering_attempts = 2 attempts per model
    Cases are excluded as 'too simple' if any of Baichuan-M1, DeepSeek-V3, or GPT-4o produces a correct answer in any of two sampling attempts (Section 3.2, Iterative Filtering).
  • consensus_attempts_for_deepseek_r1 = 8 attempts
    Cases are excluded if DeepSeek-R1 cannot reach consensus on a relevant diagnosis in eight sampling attempts (Section 3.2, Expert-AI Collaborative Verification).
assumptions (4)
  • domain assumption The published final diagnosis in each case report is correct and can serve as a single ground truth.
    Section 3.2 treats the 'Final Diagnosis' field as the answer key for all evaluation; any errors or ambiguity in the original case report transfer into the benchmark.
  • domain assumption Case reports from 10 selected top-tier medical journals are representative of challenging real-world diagnostic scenarios.
    Section 3.2 Data Collection asserts that high-impact case reports present challenging, information-dense cases, but representativeness is not demonstrated.
  • ad hoc to paper A case solved by any of Baichuan-M1, DeepSeek-V3, or GPT-4o in two attempts is too simple to test reasoning.
    Section 3.2 Iterative Filtering uses this rule to remove cases, assuming that success by these models implies knowledge recall rather than reasoning.
  • ad hoc to paper GPT-4o scoring is equivalent to expert human scoring for the identical/relevant/irrelevant taxonomy.
    Section 4.2 applies GPT-4o as judge without reporting inter-rater agreement with human physicians.

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Cite this review

Pith. "Pith review of DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models." pith.science (2026). https://pith.science/paper/XJ5LJAZE

@misc{pith2026250514107,
  author       = {Pith},
  title        = {Pith review of: DiagnosisArena: Benchmarking Diagnostic Reasoning for Large Language Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XJ5LJAZE}},
  note         = {Machine review of arXiv:2505.14107}
}
read the original abstract

The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios. To enable their safe and effective deployment in real-world healthcare settings, it is urgently necessary to benchmark the diagnostic capabilities of current models systematically. Given the limitations of existing medical benchmarks in evaluating advanced diagnostic reasoning, we present DiagnosisArena, a comprehensive and challenging benchmark designed to rigorously assess professional-level diagnostic competence. DiagnosisArena consists of 1,113 pairs of segmented patient cases and corresponding diagnoses, spanning 28 medical specialties, deriving from clinical case reports published in 10 top-tier medical journals. The benchmark is developed through a meticulous construction pipeline, involving multiple rounds of screening and review by both AI systems and human experts, with thorough checks conducted to prevent data leakage. Our study reveals that even the most advanced reasoning models, o3, o1, and DeepSeek-R1, achieve only 51.12%, 31.09%, and 17.79% accuracy, respectively. This finding highlights a significant generalization bottleneck in current large language models when faced with clinical diagnostic reasoning challenges. Through DiagnosisArena, we aim to drive further advancements in AI's diagnostic reasoning capabilities, enabling more effective solutions for real-world clinical diagnostic challenges. We provide the benchmark and evaluation tools for further research and development https://github.com/SPIRAL-MED/DiagnosisArena.

Figures

Figures reproduced from arXiv: 2505.14107 by the authors.

Figure 1
Figure 1. Performance of SOTA models on DiagnosisArena and other benchmarks. * Co-first authors † Corresponding author 1 https://github.com/SPIRAL-MED/DiagnosisArena 1 arXiv:2505.14107v4 [cs.CL] 29 May 2025 [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the DiagnosisArena Benchmark. (a) The pipeline for constructing the DiagnosisArena dataset consists of four stages: data collection from the journals, data structuring, iterative filtering of non-reasoning examples, and expert-AI collaborative verification. (b) DiagnosisArena is sourced from 10 top-tier medical journals. (c) DiagnosisArena is highly diverse, covering 28 medical specialties. (d) Diagnosis… view at source ↗
Figure 3
Figure 3. Performance of Different Models on DiagnosisArena. (a) The Top-k metric represents the proportion of cases where the correct answer is included among the Top-k predictions generated by the model, ranked in descending order of confidence. The results reveal that while the o3 outperforms others, DiagnosisArena remains a significant challenge for all existing models. (b) The MCQ presents the multiple-choice version of … view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Leakage Detection on DiagnosisArena. (a) Pre-experiment small sample Leakage Detection. For all models, the experimental results maintained a generally consistent trend across different years, with only minor fluctuations. (b) Leakage Detection on the Constructed Diagn…
Figure 5
Figure 5. Figure 5: A Case Study of DiagnosisArena. Except for o3-mini, which successfully provided the correct answer in the top 1, the other models were far from the correct answer. Analyzing DeepSeek-R1’s response, we found that, despite numerous indirect pieces of evidence supporting …
Figure 6
Figure 6. Figure 6: Pre-experiment small sample Leakage Detection. [PITH_FULL_IMAGE:figures/full_fig_p013_6.png]
Figure 7
Figure 7. Figure 7: Leakage Detection on DiagnosisArena. 13 [PITH_FULL_IMAGE:figures/full_fig_p013_7.png]

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases

    cs.CL 2026-07 conditional novelty 6.0 of 10

    A new 1,089-case multi-turn multimodal benchmark shows that even top AI medical diagnosticians are fully correct only ~34% of the time and frequently hallucinate reasoning.

  2. DeepLens Diagnosis Agent: Agentic Workflow Design Lets a Small Reasoning Model Compete with Frontier LLMs

    cs.AI 2026-05 conditional novelty 4.0 of 10

    A 7B medical model wrapped in a five-stage diagnosis workflow beats frontier LLMs on DiagnosisArena, gaining +36 points over its own single-shot baseline.

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Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.