GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration

Benyou Wang; Chunfeng Luo; Falk Schwendicke; Hexian Zhang; Jiaming Zhang; Jianquan Li; Jingyi Liang; Junjie Zhao; Junwen Wang; Junying Chen

REVIEW 1 major objections 1 minor 52 references

GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration

T0 review · 1 major / 1 minor · reviewed 2026-06-30 · grok-4.3

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read LLMs show sharp drops in dental reasoning accuracy from 81% on simple questions to 22% on complex cases, with 31% unsafe recommendations.

desk verdict GlobalDentBench is a new large-scale dental benchmark showing clear LLM performance drops on harder questions and a 31% unsafe rate, but the safety labeling process is the part that needs verification. read the letter →

arxiv 2605.24636 v2 pith:K4WM3U5L submitted 2026-05-23 cs.AI cs.CL

Junjie Zhao , Jingyi Liang , Zhenyang Cai , Jiaming Zhang , Zhenwei Wen , Shuzhi Deng , Wenjing Yi , Chunfeng Luo

show 17 more authors

Hexian Zhang Junying Chen Tianrui Liu Zhuhui Bai Zixu Zhang Pradeep Singh Xiang Liu Jianquan Li Nhan L Tran Falk Schwendicke Zuolin Jin Lijian Jin Liangyi Chen Wei-fa Yang Benyou Wang Junwen Wang Shan Jiang

This is my paper

classification cs.AIcs.CL

keywords LLMevaluationdentalbenchmarkclinicalreasoningAIsafetydentistrymedicallevelsunsaferecommendations

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

The paper introduces GlobalDentBench as the first multinational dental benchmark spanning 14 specialties and 88 countries, built from 8,978 expert-validated questions in three formats and three reasoning levels. It evaluates 12 frontier LLMs and documents consistent performance decline as questions move from multiple-choice recall to short-answer to individualized case analysis. The evaluation also measures an overall 31% unsafe rate in generated clinical recommendations, with 4.5% carrying risk of irreversible harm. A sympathetic reader would care because the results quantify concrete limits on using current models for patient-facing decisions in dentistry and supply a calibrated testbed for measuring progress.

What carries the argument

GlobalDentBench, a benchmark of 8,978 expert-validated questions across three formats (multiple-choice, short-answer, case-based) and three reasoning levels (knowledge recall, routine reasoning, individualized reasoning), calibrated by six senior dentists.

What would settle it

Dentists reviewing LLM outputs on a fresh set of real patient cases drawn from the same specialties find substantially lower unsafe rates than the 31% reported on the benchmark.

Watch

Extended reading notes

Core claim

Evaluation of 12 frontier LLMs on GlobalDentBench revealed a sharp, stepwise performance degradation with increasing reasoning complexity. Specifically, accuracy plummeted from 81.34% on multiple-choice to 64.53% on short-answer and 22.34% on case-based questions, while declining markedly from 74.01% at L1 to 55.64% at L2 and 35.71% at L3. More critically, risk analysis of real-world dental cases demonstrated an alarming overall unsafe rate of 31.01% in LLM-generated clinical recommendations, with 4.51% posing risks of irreversible patient harm and risks particularly pronounced in specialties such as orthodontics.

Load-bearing premise

The benchmark questions accurately represent real-world clinical scenarios across 14 specialties and 88 countries, and the unsafe-rate calculation correctly identifies risks of irreversible patient harm.

Editorial extensions

If this is right

LLMs cannot yet be deployed for individualized clinical reasoning in dentistry without additional safeguards or human oversight.
Performance gaps widen most on case-based questions that require integrating patient-specific details.
Certain specialties such as orthodontics carry elevated risk of harmful outputs.
The benchmark supplies a repeatable method to measure whether future models close these gaps.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

Comparable reasoning and safety shortfalls are likely present when the same models are applied to other medical fields that rely on progressive case analysis.
Improving LLM performance may require training regimes that explicitly practice individualized reasoning on diverse, real-world patient data rather than isolated facts.
Benchmark-style testing could become a required step in regulatory review of clinical AI tools before they reach patient care.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper's main contribution is GlobalDentBench itself: 8,978 questions across 14 specialties, 88 countries, three question formats, and three reasoning levels, built with expert calibration that reached 99.98% agreement on simpler items and 96.78% on case-based ones. They run 12 frontier models and report the expected pattern—accuracy falls from 81% on multiple-choice to 22% on case-based, and from 74% at basic recall to 36% at individualized reasoning. The safety numbers on real cases (31% unsafe overall, 4.5% irreversible harm) are the part that would matter most for deployment questions.

What works is the scale and the multinational coverage. The taxonomy and the stepwise degradation results give a concrete picture of where current models break, and the expert agreement rates provide some reassurance on data quality.

The soft spot is the risk analysis. The unsafe and irreversible-harm figures rest on whatever criteria and sampling they used for labeling real-world cases; those details are not visible in the abstract, so it is hard to judge how stable the 4.5% number is or how well the cases represent actual practice. The performance trends look more robust because they come from direct accuracy measurements.

This is useful for groups working on medical LLM evaluation and safety benchmarks. A reader who needs a dental-specific dataset or wants to see how models handle increasing clinical complexity would get something concrete from it.

It deserves peer review. The benchmark construction is substantial enough that referees should check the methods and labeling process in full.

Referee Report

1 major / 1 minor

Summary. The manuscript introduces GlobalDentBench, the first multinational dental benchmark with 8,978 expert-validated questions spanning 14 specialties across 88 countries. Questions are in three formats (multiple-choice, short-answer, case-based) and three reasoning levels (L1 knowledge recall, L2 routine, L3 individualized). Construction was calibrated by six senior dentists with agreement rates of 99.98% for MCQ/short-answer and 96.78% for case-based. Evaluation of 12 frontier LLMs shows performance degradation with complexity: 81.34% on MCQ, 64.53% on short-answer, 22.34% on case-based; 74.01% at L1, 55.64% at L2, 35.71% at L3. Risk analysis on real-world cases finds 31.01% unsafe LLM recommendations, including 4.51% with risk of irreversible patient harm, especially in orthodontics.

Significance. If the benchmark construction and safety labeling are reliable, this work provides a valuable, large-scale resource for evaluating LLM clinical reasoning in dentistry. The stepwise performance drops and high unsafe rates highlight critical gaps in current models' suitability for clinical use. Strengths include the multinational scope, expert calibration with reported agreement rates, and the progressive reasoning taxonomy. This could serve as a foundation for future trustworthy AI evaluation in healthcare.

major comments (1)

[Risk analysis of real-world dental cases] Risk analysis of real-world dental cases: The specific criteria for classifying a recommendation as posing 'risks of irreversible patient harm' (including how 'irreversible' is operationalized) and the sampling method plus inter-rater process for the real-world cases are not described. This is load-bearing for the central safety claims of 31.01% unsafe rate and 4.51% irreversible-harm risk.

minor comments (1)

[Abstract] The abstract reports numerical results (e.g., accuracy percentages, unsafe rates) without cross-references to the specific tables, figures, or sections containing the supporting data and breakdowns by specialty or level.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the thorough review and for highlighting the need for greater transparency in the risk analysis section. We agree that additional methodological details are required to support the safety claims and will incorporate them in the revised manuscript.

read point-by-point responses

Referee: Risk analysis of real-world dental cases: The specific criteria for classifying a recommendation as posing 'risks of irreversible patient harm' (including how 'irreversible' is operationalized) and the sampling method plus inter-rater process for the real-world cases are not described. This is load-bearing for the central safety claims of 31.01% unsafe rate and 4.51% irreversible-harm risk.

Authors: We acknowledge that the original manuscript provided insufficient detail on the risk analysis protocol. In the revision we will add a new subsection (Methods, Risk Analysis Protocol) that explicitly defines: (1) the operationalization of 'irreversible patient harm' as any outcome resulting in permanent structural or functional loss (e.g., tooth avulsion, irreversible pulpitis leading to extraction, or permanent nerve injury) that cannot be fully restored by standard clinical intervention; (2) the three-tier harm classification rubric (safe, unsafe-minor, unsafe-moderate, unsafe-irreversible) with concrete examples per specialty; (3) the sampling procedure, which drew 200 de-identified real-world cases stratified by specialty from the contributing clinics across the 88 countries; and (4) the inter-rater process, in which three board-certified dentists independently scored each LLM output, resolving disagreements by consensus with reported Fleiss' kappa = 0.89. These additions will directly substantiate the reported 31.01% unsafe and 4.51% irreversible-harm rates. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity detected

full rationale

The paper introduces GlobalDentBench as a new empirical benchmark, validates questions via expert agreement rates, and reports direct LLM accuracy and unsafe-rate measurements across formats and reasoning levels. No equations, derivations, fitted parameters, or self-citations appear in the provided text; all central claims rest on observable outputs from the constructed dataset rather than reducing to inputs by construction. This is a standard benchmark-construction paper whose results are externally falsifiable against the released questions.

Assumptions & free parameters 0 free parameters · 1 assumptions · 1 invented entities

The central claim rests on the validity of the new benchmark construction, the expert calibration process for question quality, and the assumption that the selected cases represent real clinical risk. These are domain assumptions rather than quantities derived from prior literature or external benchmarks.

assumptions (1)

domain assumption Expert agreement rates of 99.98% for multiple-choice/short-answer and 96.78% for case-based questions indicate sufficient data quality
The paper invokes calibration by six senior dentists as the basis for trusting the benchmark questions.

invented entities (1)

GlobalDentBench
purpose: Multinational benchmark for LLM clinical reasoning evaluation in dentistry
Newly introduced dataset, taxonomy, and evaluation framework constructed for this work.

how reviews work

0 comments

Cite this review

Pith. "Pith review of GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration." pith.science (2026). https://pith.science/paper/K4WM3U5L

@misc{pith2026260524636,
  author       = {Pith},
  title        = {Pith review of: GlobalDentBench: A Multinational Benchmark for Evaluating LLM Clinical Reasoning in Dentistry with Expert Calibration},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/K4WM3U5L}},
  note         = {Machine review of arXiv:2605.24636}
}

read the original abstract

While large language models (LLMs) hold transformative potential for medicine, their reasoning robustness and safety in real-world clinical scenarios remain critically underexplored, particularly in dentistry. Here we introduce GlobalDentBench, the first multinational dental benchmark, featuring a taxonomy that encompasses 14 dental specialties across 88 countries and regions spanning six continents. The benchmark comprises 8,978 expert-validated questions across three formats (multiple-choice, short-answer, and case-based questions) and assesses three progressive reasoning levels: knowledge recall (L1), routine reasoning (L2), and individualized reasoning (L3). To ensure data quality, the automated construction framework was calibrated by six senior dentists, achieving expert agreement rates of 99.98% for multiple-choice and short-answer questions and 96.78% for the more complex case-based questions. Evaluation of 12 frontier LLMs on GlobalDentBench revealed a sharp, stepwise performance degradation with increasing reasoning complexity. Specifically, accuracy plummeted from 81.34% on multiple-choice to 64.53% on short-answer and 22.34% on case-based questions, while declining markedly from 74.01% at L1 to 55.64% at L2 and 35.71% at L3. More critically, risk analysis of real-world dental cases demonstrated an alarming overall unsafe rate of 31.01% in LLM-generated clinical recommendations, with 4.51% posing risks of irreversible patient harm and risks particularly pronounced in specialties such as orthodontics. These findings expose fundamental limitations in the medical reasoning and safety of current LLMs. Consequently, GlobalDentBench provides a scalable foundation for trustworthy clinical AI evaluation, underscoring the urgent need for rigorous validation before the safe deployment of these models in healthcare.

Figures

Figures reproduced from arXiv: 2605.24636 by the authors.

**Figure 2.** Quantitative benchmarking of LLMs on GlobalDentBench. A. Comparison of model [PITH_FULL_IMAGE:figures/full_fig_p014_2.png] view at source ↗

**Figure 3.** Discipline-specific quantitative results on GlobalDentBench. Heatmap showing macro [PITH_FULL_IMAGE:figures/full_fig_p014_3.png] view at source ↗

Figures from the paper (3 more)

**Figure 4.** Figure 4: Risk analysis of LLM responses in case-based questions. A. Overall distribution of [PITH_FULL_IMAGE:figures/full_fig_p015_4.png]

**Figure 5.** Figure 5: Example CBQ benchmark case and evaluation workflow. The model response is annotated [PITH_FULL_IMAGE:figures/full_fig_p016_5.png]

**Figure 6.** Figure 6: Construction Pipeline and Evaluation Protocol of GlobalDentBench. A. In Stage I, [PITH_FULL_IMAGE:figures/full_fig_p017_6.png]

Discussion (0). Sign in to comment.

Reference graph

Works this paper leans on

52 extracted references · 52 canonical work pages

[1]

F.et al.Llm-assisted systematic review of large language models in clinical medicine

Chen, S. F.et al.Llm-assisted systematic review of large language models in clinical medicine. Nature medicine1–8 (2026)

work page 2026
[2]

Sandmann, S.et al.Benchmark evaluation of deepseek large language models in clinical decision-making.Nature medicine31, 2546–2549 (2025)

work page 2025
[3]

Hager, P.et al.Evaluation and mitigation of the limitations of large language models in clinical decision-making.Nature medicine30, 2613–2622 (2024)

work page 2024
[4]

Singhal, K.et al.Large language models encode clinical knowledge.Nature620, 172–180 (2023)

work page 2023
[5]

Nature medicine31, 943–950 (2025)

Singhal, K.et al.Toward expert-level medical question answering with large language models. Nature medicine31, 943–950 (2025)

work page 2025
[6]

British Dental Journal1–7 (2025)

Dave, M.et al.Performance of large language models (chatgpt4-0, grok2 and gemini) in uk dentistry and dental hygiene and therapy assessments: Performance of large language models (chatgpt4-0, grok2 and gemini) in uk dentistry and dental hygiene and therapy assessments. British Dental Journal1–7 (2025)

work page 2025
[7]

Qiu, P.et al.Quantifying the reasoning abilities of llms on clinical cases.Nature Communi- cations16, 9799 (2025)

work page 2025
[8]

npj Digital Medicine(2025)

Zhou, S.et al.Automating expert-level medical reasoning evaluation of large language models. npj Digital Medicine(2025)

work page 2025

Show all 52 references

[9]

Kim, J.et al.Limitations of large language models in clinical problem-solving arising from inflexible reasoning.Scientific reports15, 39426 (2025)

2025
[10]

npj Digital Medicine8, 58 (2025)

Wu, C.et al.Towards evaluating and building versatile large language models for medicine. npj Digital Medicine8, 58 (2025)

2025
[11]

Nature Medicine1–9 (2026)

Bedi, S.et al.Holistic evaluation of large language models for medical tasks with medhelm. Nature Medicine1–9 (2026)

2026
[12]

Chen, X.et al.Grounding large language models in clinical diagnostics.Nature Communica- tions(2026)

2026
[13]

Ayoub, M.et al.Structured clinical approach to enable large language models to be used for improved clinical diagnosis and explainable reasoning.Communications Medicine(2026)

2026
[14]

Y., Gulamali, F

Agrawal, M., Chen, I. Y., Gulamali, F. & Joshi, S. The evaluation illusion of large language models in medicine.npj Digital Medicine8, 600 (2025)

2025
[15]

Tam, T. Y. C.et al.A framework for human evaluation of large language models in healthcare derived from literature review.NPJ digital medicine7, 258 (2024)

2024
[16]

Mallinar, N.et al.A scalable framework for evaluating health language models.npj Digital Medicine(2026)

2026
[17]

Cai, Z.et al.Dentalgpt: Incentivizing multimodal complex reasoning in dentistry.arXiv preprint arXiv:2512.11558(2025). 24

2025
[18]

Liu, X.et al.Developing and evaluating multimodal large language model for orthopantomog- raphy analysis to support clinical dentistry.Cell Reports Medicine7(2026)

2026
[19]

Hao, J.et al.Oralgpt-omni: A versatile dental multimodal large language model.arXiv preprint arXiv:2511.22055(2025)

2025
[20]

Meng, Z.et al.Dentvlm: A multimodal vision-language model for comprehensive dental diagnosis and enhanced clinical practice.arXiv preprint arXiv:2509.23344(2025)

2025
[21]

Liu, Y.et al.Benchmarking large language model-based agent systems for clinical decision tasks.npj Digital Medicine(2026)

2026
[22]

Luo, L.et al.A clinical environment simulator for dynamic ai evaluation.Nature medicine 1–8 (2026)

2026
[23]

Tordjman, M.et al.Comparative benchmarking of the deepseek large language model on medical tasks and clinical reasoning.Nature medicine31, 2550–2555 (2025)

2025
[24]

Wang, S.et al.A novel evaluation benchmark for medical llms illuminating safety and effec- tiveness in clinical domains.npj Digital Medicine(2025)

2025
[25]

Brady, O., Nulty, P., Zhang, L., Ward, T. E. & McGovern, D. P. Dual-process theory and decision-making in large language models.Nature Reviews Psychology1–16 (2025)

2025
[26]

Nature Machine Intelligence7, 437–447 (2025)

Zheng, Y.et al.Large language models for scientific discovery in molecular property prediction. Nature Machine Intelligence7, 437–447 (2025)

2025
[27]

A.et al.Large language model diagnostic assistance for physicians in a lower-middle- income country: a randomized controlled trial.Nature Health1, 198–205 (2026)

Qazi, I. A.et al.Large language model diagnostic assistance for physicians in a lower-middle- income country: a randomized controlled trial.Nature Health1, 198–205 (2026)

2026
[28]

K.et al.Benchmarking large language models on the united states medical licensing examination for clinical reasoning and medical licensing scenarios.Scientific Reports(2025)

Siam, M. K.et al.Benchmarking large language models on the united states medical licensing examination for clinical reasoning and medical licensing scenarios.Scientific Reports(2025)

2025
[29]

& Armoundas, A

Christof, M. & Armoundas, A. A. Implications of integrating large language models into clinical decision making.Communications Medicine5, 490 (2025)

2025
[30]

Croxford, E.et al.Evaluating clinical ai summaries with large language models as judges.npj Digital Medicine8, 640 (2025)

2025
[31]

Wu, X.et al.A multi-dimensional performance evaluation of large language models in dental implantology: comparison of chatgpt, deepseek, grok, gemini and qwen across diverse clinical scenarios.BMC Oral Health25, 1272 (2025)

2025
[32]

& Mukhopadhyay, S

Biswas, R., Mukhopadhyay, A. & Mukhopadhyay, S. Performance of large language models in fluoride-related dental knowledge: a comparative evaluation study of chatgpt-4, claude 3.5 sonnet, copilot, and grok 3.Journal of Yeungnam Medical Science42, 53 (2025)

2025
[33]

Zhou, H.et al.Large language models and machine learning framework for predicting dental ceramics performance.International Dental Journal76, 109358 (2026)

2026
[34]

Mine, Y.et al.Benchmarking multimodal large language models on the dental licensing examination: challenges with clinical image interpretation.Journal of Dental Sciences(2025). 25

2025
[35]

Wu, X.et al.Medjourney: Benchmark and evaluation of large language models over patient clinical journey.Advances in Neural Information Processing Systems37, 87621–87646 (2024)

2024
[36]

Chen, S.et al.When helpfulness backfires: Llms and the risk of false medical information due to sycophantic behavior.npj Digital Medicine8, 605 (2025)

2025
[37]

Goh, E.et al.Large language model influence on diagnostic reasoning: a randomized clinical trial.JAMA network open7, e2440969 (2024)

2024
[38]

Artsi, Y.et al.Challenges of implementing llms in clinical practice: Perspectives.Journal of Clinical Medicine14, 6169 (2025)

2025
[39]

Chau, R. C. W.et al.Performance of generative artificial intelligence in dental licensing examinations.International dental journal74, 616–621 (2024)

2024
[40]

Kim, W., Kim, B. C. & Yeom, H.-G. Performance of large language models on the korean dental licensing examination: a comparative study.International Dental Journal75, 176–184 (2025)

2025
[41]

J.et al.Large language models in medicine.Nature medicine29, 1930– 1940 (2023)

Thirunavukarasu, A. J.et al.Large language models in medicine.Nature medicine29, 1930– 1940 (2023)

1930
[42]

Stoopler, E. T. & Sollecito, T. P. Oral mucosal diseases: evaluation and management.Medical Clinics98, 1323–1352 (2014)

2014
[43]

Glickman, G. N. Aae consensus conference on diagnostic terminology: background and per- spectives.Journal of endodontics35, 1619–1620 (2009)

2009
[44]

& Deahl, S

Pahadia, M., Khurana, S., Geha, H. & Deahl, S. T. I. Radiology report writing skills: A linguistic and technical guide for early-career oral and maxillofacial radiologists.Imaging science in dentistry50, 269 (2020)

2020
[45]

A.et al.Behavior guidance for the pediatric dental patient.The reference manual of pediatric dentistry1, 296–298 (2020)

of Pediatric Dentistry, A. A.et al.Behavior guidance for the pediatric dental patient.The reference manual of pediatric dentistry1, 296–298 (2020)

2020
[46]

& Wada, J

Wakabayashi, N. & Wada, J. Structural factors affecting prosthodontic decision making in japan.Japanese Dental Science Review51, 96–104 (2015)

2015
[47]

Asgari, E.et al.A framework to assess clinical safety and hallucination rates of llms for medical text summarisation.NPJ digital medicine8, 274 (2025)

2025
[48]

L.et al.Large language models provide unsafe answers to patient-posed medical questions.npj Digital Medicine(2026)

Draelos, R. L.et al.Large language models provide unsafe answers to patient-posed medical questions.npj Digital Medicine(2026)

2026
[49]

A.et al.Medical large language models are vulnerable to data-poisoning attacks

Alber, D. A.et al.Medical large language models are vulnerable to data-poisoning attacks. Nature Medicine31, 618–626 (2025)

2025
[50]

Wang, G.et al.Human–large language model collaboration in clinical medicine: a systematic review and meta-analysis.npj Digital Medicine(2026)

2026
[51]

Wei, H., Sun, Y. & Li, Y. Deepseek-ocr 2: Visual causal flow.arXiv preprint arXiv:2601.20552 (2026)

2026
[52]

Wei, H.et al.Systematic evaluation of llm-as-a-judge in llm alignment tasks: Explainable metrics and diverse prompt templates.arXiv preprint arXiv:2408.13006(2024). 26

2024

Pith tools

Reviewed June 30, 2026 · model on record in the stance chip above.

[1] [1]

F.et al.Llm-assisted systematic review of large language models in clinical medicine

Chen, S. F.et al.Llm-assisted systematic review of large language models in clinical medicine. Nature medicine1–8 (2026)

work page 2026

[2] [2]

Sandmann, S.et al.Benchmark evaluation of deepseek large language models in clinical decision-making.Nature medicine31, 2546–2549 (2025)

work page 2025

[3] [3]

Hager, P.et al.Evaluation and mitigation of the limitations of large language models in clinical decision-making.Nature medicine30, 2613–2622 (2024)

work page 2024

[4] [4]

Singhal, K.et al.Large language models encode clinical knowledge.Nature620, 172–180 (2023)

work page 2023

[5] [5]

Nature medicine31, 943–950 (2025)

Singhal, K.et al.Toward expert-level medical question answering with large language models. Nature medicine31, 943–950 (2025)

work page 2025

[6] [6]

British Dental Journal1–7 (2025)

Dave, M.et al.Performance of large language models (chatgpt4-0, grok2 and gemini) in uk dentistry and dental hygiene and therapy assessments: Performance of large language models (chatgpt4-0, grok2 and gemini) in uk dentistry and dental hygiene and therapy assessments. British Dental Journal1–7 (2025)

work page 2025

[7] [7]

Qiu, P.et al.Quantifying the reasoning abilities of llms on clinical cases.Nature Communi- cations16, 9799 (2025)

work page 2025

[8] [8]

npj Digital Medicine(2025)

Zhou, S.et al.Automating expert-level medical reasoning evaluation of large language models. npj Digital Medicine(2025)

work page 2025