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arxiv: 2508.04325 · v2 · submitted 2025-08-06 · 💻 cs.CL · cs.AI· cs.CV· cs.LG· cs.MM

Beyond the Leaderboard: Rethinking Medical Benchmarks for Large Language Models

Wenting Chen , Guo Yu , Yiu-Fai Cheung , Meidan Ding , Jie Liu , Zizhan Ma , Wenxuan Wang , Linlin Shen This is my paper

Pith reviewed 2026-05-19 00:35 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.CVcs.LGcs.MM
keywords medical benchmarkslarge language modelsLLM evaluationclinical fidelitydata contaminationsafety assessmenthealthcare AIbenchmark lifecycle
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0 comments X

The pith

Existing medical LLM benchmarks show deep disconnects from clinical practice, data contamination risks, and neglected safety tests.

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

The paper introduces MedCheck, a framework that breaks benchmark development into five lifecycle stages and checks them against 46 medically specific criteria. When applied to 53 current benchmarks, it finds most lack ties to real clinical work, carry high risks of data leakage or contamination, and skip key safety checks such as robustness and uncertainty handling. A reader would care because these shortfalls mean leaderboard scores may not reflect whether an LLM would actually help or harm in medical settings. If the analysis holds, it points to the need for more careful, standardized ways to build and judge medical AI evaluations.

Core claim

We introduce MedCheck, the first lifecycle-oriented assessment framework specifically designed for medical benchmarks. Our framework deconstructs a benchmark's development into five continuous stages, from design to governance, and provides a comprehensive checklist of 46 medically-tailored criteria. Using MedCheck, we conducted an in-depth empirical evaluation of 53 medical LLM benchmarks. Our analysis uncovers widespread, systemic issues, including a profound disconnect from clinical practice, a crisis of data integrity due to unmitigated contamination risks, and a systematic neglect of safety-critical evaluation dimensions like model robustness and uncertainty awareness.

What carries the argument

MedCheck, the five-stage lifecycle framework equipped with 46 medically tailored criteria that audits benchmarks from initial design through ongoing governance.

If this is right

  • Benchmarks can be audited stage by stage for alignment with actual clinical workflows.
  • Future benchmark creators can add explicit steps to reduce data contamination.
  • Safety evaluations must routinely include tests for model robustness and uncertainty awareness.
  • Standardized checklists can replace ad-hoc approaches in medical AI assessment.
  • Healthcare AI development can shift toward more transparent and reliable performance measures.

Where Pith is reading between the lines

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

  • Widespread adoption could reorder current leaderboard standings once the new criteria are applied.
  • The same lifecycle approach might transfer to benchmark design in other high-stakes fields such as law or autonomous systems.
  • Researchers could run follow-up studies to check whether MedCheck scores predict better patient outcomes in controlled trials.
  • Periodic updates to the criteria would likely be needed as medical AI capabilities and risks evolve.

Load-bearing premise

The 46 criteria are assumed to capture all essential dimensions of clinical fidelity, data management, and safety without major gaps or skewed priorities.

What would settle it

Finding that benchmarks scoring low on MedCheck still produce safe, accurate results in real clinical deployments, or that high-scoring ones fail in practice, would test the framework's core claims.

Figures

Figures reproduced from arXiv: 2508.04325 by Guo Yu, Jie Liu, Linlin Shen, Meidan Ding, Wenting Chen, Wenxuan Wang, Yiu-Fai Cheung, Zizhan Ma.

Figure 1
Figure 1. Figure 1: (a) Overall and (b) phase-by-phase perfor [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: The proposed five-phase lifecycle for medical benchmark engineering. This model illustrates the [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: The three-step methodology employed in this study. Our approach involved (1) developing the 46-criteria [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Temporal Distribution of Benchmarks by Task [PITH_FULL_IMAGE:figures/full_fig_p015_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Publication Year Distribution of the 53 Medi [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 8
Figure 8. Figure 8: Disease Distribution Based on ICD-11 Classi [PITH_FULL_IMAGE:figures/full_fig_p016_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Medical Specialty Distribution Based on the [PITH_FULL_IMAGE:figures/full_fig_p017_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Performance score rates (%) for 14 medical [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 13
Figure 13. Figure 13: Performance on Domain Experts Involve￾ment Not men�oned 53% Men�oned the importance, but did not adherence 24% Clearly indicated 23% [PITH_FULL_IMAGE:figures/full_fig_p018_13.png] view at source ↗
Figure 12
Figure 12. Figure 12: Performance on Clarity of Application Sce [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 15
Figure 15. Figure 15: Performance on Multi-dimensional Evalua [PITH_FULL_IMAGE:figures/full_fig_p019_15.png] view at source ↗
Figure 17
Figure 17. Figure 17: Scoring Performance of the 53 Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p019_17.png] view at source ↗
Figure 16
Figure 16. Figure 16: Performance on Consideration of Safety and [PITH_FULL_IMAGE:figures/full_fig_p019_16.png] view at source ↗
Figure 18
Figure 18. Figure 18: Coverage of disease types, medical de￾partments, and overall performance across 53 medical benchmarks. B.7 Statistics About Phase III: Technical Implementation and Evaluation Methodology This is the operational backbone. It consists of 8 items, encompassing the development of accessi￾ble, reproducible evaluation scripts and the selec￾tion of metrics that go beyond simple accuracy to assess deeper capabili… view at source ↗
Figure 19
Figure 19. Figure 19: Performance on Dataset diversity Not Men�oned 43% Detec�on Without Mi�ga�on 49% Clearly Described Detec�on and Mi�ga�on Measures 8% [PITH_FULL_IMAGE:figures/full_fig_p021_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Performance on Data Contamination Preven [PITH_FULL_IMAGE:figures/full_fig_p021_20.png] view at source ↗
Figure 24
Figure 24. Figure 24: Performance on Uncertainty Evaluation lidity (i.e., whether it measures the claimed latent capabilities), as well as its ability to distinguish performance differences among models of vary￾ing proficiency levels. Among the 53 benchmarks evaluated, the average score rate for this phase was 48.8% (5.9 out of 12), with a mean of 0.5 points per item—representing the lowest performance among the five phases. A… view at source ↗
Figure 25
Figure 25. Figure 25: Scoring Performance of the 53 Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p022_25.png] view at source ↗
Figure 27
Figure 27. Figure 27: Performance on Internal consistency Statistical Significance Reporting. This crite￾rion evaluates whether statistical significance or uncertainty—such as confidence intervals or p￾values—was reported when comparing model per￾formances. In the absence of such analyses, ob￾served performance differences may arise from sample randomness or dataset variability rather than genuine model capability disparities.… view at source ↗
Figure 26
Figure 26. Figure 26: Performance on Correlation with Clinical [PITH_FULL_IMAGE:figures/full_fig_p023_26.png] view at source ↗
Figure 29
Figure 29. Figure 29: Scoring Performance of the 53 Benchmarks [PITH_FULL_IMAGE:figures/full_fig_p024_29.png] view at source ↗
Figure 30
Figure 30. Figure 30: Performance on Discussion of Limitations [PITH_FULL_IMAGE:figures/full_fig_p024_30.png] view at source ↗
Figure 34
Figure 34. Figure 34: Top 15 Benchmarks by Score in Phase IV [PITH_FULL_IMAGE:figures/full_fig_p025_34.png] view at source ↗
Figure 31
Figure 31. Figure 31: Top 15 Benchmarks by Score in Phase I 18 18.5 19 19.5 20 20.5 21 21.5 MedChain AfriMed-QAMEDIC BRIDGE MedR-Bench VQA-RAD ReasonMed ClinicBench MedAgentBench MedS-Bench GMAI-MMBench Slake-VQA MedOdyssey MedCalc-Bench EHRNoteQA [PITH_FULL_IMAGE:figures/full_fig_p025_31.png] view at source ↗
Figure 32
Figure 32. Figure 32: Top 15 Benchmarks by Score in Phase II 8 9 10 11 12 13 14 15 MEDIC AgentClinic HealthBench ReasonMed MedRisk EndoBenchMVME MedAgentsBench MedS-Bench GMAI-MMBench MedChain TRIALPANORAMA MedCalc-Bench MedAgentBench CHBench [PITH_FULL_IMAGE:figures/full_fig_p025_32.png] view at source ↗
Figure 33
Figure 33. Figure 33: Top 15 Benchmarks by Score in Phase III C Full Evaluation Criteria C.1 Design and Conceptualization C.1.1 Purpose and Intent 1. Clarity of Evaluation Objectives 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 HealthBenchMEDIC EHRNoteQABRIDGE ClinicBench MedAgentBench AgentClinic MedOdyssey MedCalc-BenchMVME MedR-Bench TRIALPANORAMA MedRisk MedAgentsBench CHBench [PITH_FULL_IMAGE:figures/full_fig_p025_33.png] view at source ↗
read the original abstract

Large language models (LLMs) show significant potential in healthcare, prompting numerous benchmarks to evaluate their capabilities. However, concerns persist regarding the reliability of these benchmarks, which often lack clinical fidelity, robust data management, and safety-oriented evaluation metrics. To address these shortcomings, we introduce MedCheck, the first lifecycle-oriented assessment framework specifically designed for medical benchmarks. Our framework deconstructs a benchmark's development into five continuous stages, from design to governance, and provides a comprehensive checklist of 46 medically-tailored criteria. Using MedCheck, we conducted an in-depth empirical evaluation of 53 medical LLM benchmarks. Our analysis uncovers widespread, systemic issues, including a profound disconnect from clinical practice, a crisis of data integrity due to unmitigated contamination risks, and a systematic neglect of safety-critical evaluation dimensions like model robustness and uncertainty awareness. Based on these findings, MedCheck serves as both a diagnostic tool for existing benchmarks and an actionable guideline to foster a more standardized, reliable, and transparent approach to evaluating AI in healthcare.

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.

Referee Report

3 major / 1 minor

Summary. The paper claims to introduce MedCheck, the first lifecycle-oriented assessment framework for medical benchmarks with 46 criteria across five stages from design to governance. Applying it to 53 medical LLM benchmarks reveals widespread systemic issues including disconnect from clinical practice, data integrity problems due to contamination, and neglect of safety dimensions like robustness and uncertainty awareness. It positions MedCheck as both diagnostic and guideline for better evaluation.

Significance. If the framework's criteria are shown to be valid and reliable, this could have substantial impact by shifting the field toward more clinically relevant and safe benchmark practices. The broad audit provides a valuable snapshot of current shortcomings in medical LLM evaluation.

major comments (3)
  1. [MedCheck framework description] The construction of the 46 criteria lacks any described methodology for derivation, such as synthesis from existing literature, input from medical experts, or validation through pilot studies and inter-rater reliability measures. This is load-bearing for the central claim, as the identification of 'systemic issues' in the 53 benchmarks depends on these criteria accurately reflecting clinical fidelity, data management, and safety without author-specific biases or omissions.
  2. [Empirical evaluation of 53 benchmarks] While the abstract states that the analysis uncovers 'widespread, systemic issues,' there are no quantitative results, tables summarizing failure rates per criterion or stage, or statistical evidence provided in the high-level description. This makes it difficult to assess the strength of the 'profound disconnect' and 'crisis of data integrity' claims.
  3. [Audit process] Details on how the evaluation was performed, including the number of raters, their expertise, blinding procedures, or resolution of disagreements, are not mentioned. This raises concerns about the reproducibility and objectivity of the findings regarding safety-critical dimensions.
minor comments (1)
  1. [Abstract] Consider adding a brief note on how the five stages were selected to enhance clarity for readers unfamiliar with lifecycle models in benchmark development.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We sincerely thank the referee for their constructive and detailed feedback on our manuscript. The comments identify key areas where additional transparency and rigor will strengthen the presentation of MedCheck and our audit findings. We respond to each major comment below and indicate the revisions we will make.

read point-by-point responses

  1. Referee: [MedCheck framework description] The construction of the 46 criteria lacks any described methodology for derivation, such as synthesis from existing literature, input from medical experts, or validation through pilot studies and inter-rater reliability measures. This is load-bearing for the central claim, as the identification of 'systemic issues' in the 53 benchmarks depends on these criteria accurately reflecting clinical fidelity, data management, and safety without author-specific biases or omissions.

    Authors: We agree that an explicit account of criterion derivation is necessary to support the framework's validity. The 46 criteria were synthesized from a systematic review of clinical practice guidelines, AI evaluation literature, and regulatory documents on medical AI safety, with input from co-authors who have clinical and technical expertise. To address the referee's concern, we will add a dedicated subsection in the Methods describing this synthesis process, citing the primary sources, and noting the internal review steps used to refine the criteria. This addition will clarify the grounding of the framework and reduce any perception of author-specific bias. revision: yes

  2. Referee: [Empirical evaluation of 53 benchmarks] While the abstract states that the analysis uncovers 'widespread, systemic issues,' there are no quantitative results, tables summarizing failure rates per criterion or stage, or statistical evidence provided in the high-level description. This makes it difficult to assess the strength of the 'profound disconnect' and 'crisis of data integrity' claims.

    Authors: We accept that the main text currently emphasizes qualitative synthesis over aggregate statistics. The full manuscript contains per-benchmark annotations in the supplementary materials, but we will add a new summary table to the Results section that reports the percentage of benchmarks failing each of the five stages and selected high-impact criteria (e.g., contamination controls and robustness testing). This table will include simple counts and proportions to quantify the systemic issues and allow readers to evaluate the strength of our conclusions directly. revision: yes

  3. Referee: [Audit process] Details on how the evaluation was performed, including the number of raters, their expertise, blinding procedures, or resolution of disagreements, are not mentioned. This raises concerns about the reproducibility and objectivity of the findings regarding safety-critical dimensions.

    Authors: We acknowledge the importance of documenting the audit procedure for reproducibility. The evaluation was performed by the four authors, each with relevant expertise in medical AI, benchmark design, or clinical informatics; disagreements on criterion application were resolved through iterative discussion until consensus was reached. Because the source material consisted of publicly available benchmark papers and documentation, formal blinding was not required. We will expand the Methods section to include these details, along with a brief description of the consensus process, to improve transparency and allow future replication efforts. revision: yes

Circularity Check

0 steps flagged

No significant circularity: MedCheck is an independent framework applied to external benchmarks

full rationale

The paper introduces MedCheck as a new lifecycle-oriented framework that deconstructs benchmark development into five stages and supplies a checklist of 46 criteria; this construction is presented as an original contribution rather than derived from the 53 benchmarks under evaluation. The subsequent empirical audit applies the framework to external benchmarks, uncovering issues such as disconnect from clinical practice and data contamination risks. No equations, fitted parameters, self-citations, or ansatzes are invoked to force the criteria or results back onto the input benchmarks by construction. The derivation chain remains self-contained because the framework functions as an independent diagnostic lens rather than a self-referential mapping of its own evaluation targets.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the untested assumption that the chosen five stages and 46 criteria form a complete and unbiased audit instrument for medical benchmarks.

axioms (1)
  • domain assumption Benchmark development can be meaningfully decomposed into five continuous stages from design to governance.
    Framework construction in the abstract.
invented entities (1)
  • MedCheck framework no independent evidence
    purpose: Lifecycle-oriented assessment of medical LLM benchmarks
    Newly introduced diagnostic tool with 46 criteria.

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Forward citations

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Reference graph

Works this paper leans on

57 extracted references · 57 canonical work pages · cited by 2 Pith papers · 3 internal anchors

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    In this Equation, Ndisease represents the num- ber of diseases in the ICD-11 standard, specifically the first 23 diseases, serving as the benchmark for the types of diseases considered. Ndepartment de- notes the number of medical departments, typi- cally referring to the medical specialties included in the model’s evaluation. The variableN benchmark disea...

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    did not mention the issue at all. 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 AfriMed-QA GMAI-MMBench MedRisk Asclepius MVME MMMU(Health &… VQA-Med HealthBench BRIDGE OmniMedVQA webMedQA MedCalc-Bench MedChain MedJourney CMB CLIMB MedMCQA ReasonMed COGNET-MD CheXpert CHBench SLAKE MediQ MedExQA MLEC-QA LLMEval-Med PathMMU Disease Type Coverage 0 0.1 0.2 0.3 0.4 0.5 0.6...

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    These findings highlight a lack of rigorous statistical stan- dards in current benchmark design

    provided mean and standard deviation across multiple runs, they did not employ formal statis- tical tests to compare model performance. These findings highlight a lack of rigorous statistical stan- dards in current benchmark design. Incorporating structured multi-run strategies and statistical signif- 23 icance testing is critical to enhance the reliabili...

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    • Justification: Clearly defined evaluation objectives can avoid ambiguity, facili- tating subsequent data collection, task design, and metric selection

    Clarity of Evaluation Objectives 6 6.5 7 7.5 8 8.5 9 9.5 10 10.5 HealthBench MEDIC EHRNoteQA BRIDGE ClinicBench MedAgentBench AgentClinicMedOdysseyMedCalc-Bench MVME MedR-Bench TRIALPANORAMA MedRisk MedAgentsBench CHBench Figure 34: Top 15 Benchmarks by Score in Phase IV 9 10 11 12 13 14 15 16 17 18 MedS-Bench MEDICBRIDGE MedAgentBench HealthBench MedSafe...

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    • Justification: Linking the benchmark to real-world application scenarios en- sures that the results are meaningful

    Clarity of Application Scenario • Explanation: Benchmark developers should clearly describes the specific med- 25 ical application scenarios it corresponds to and explains the potential value. • Justification: Linking the benchmark to real-world application scenarios en- sures that the results are meaningful. It facilitates the validation of model’s effec...

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  14. [14]

    • Justification: Demonstrating the unique- ness demonstrates the necessity and jus- tification of the new benchmark

    Uniqueness and Novelty • Explanation: By comparing with rele- vant benchmarks, benchmark developers should explain their contributions and the uniqueness of the benchmark, such as filling a gap and proposing new evalu- ation methodology. • Justification: Demonstrating the unique- ness demonstrates the necessity and jus- tification of the new benchmark. It...

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    • Scoring: – 0: Does not define the target LLM capability

    Target Capability of Evaluation • Explanation: Benchmark developers should clearly define the capability of LLMs intended to evaluate (e.g., text gen- eration, multimodal understanding) • Justification: Clearly defining the tar- geted capability helps clarify the scope of the benchmark, which can prevent mis- using the benchmark. • Scoring: – 0: Does not ...

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    • Justification: By clearly defining the medical scope, it helps users better un- derstand the breath and depth of the cov- erage of the benchmark

    Medical Domain Coverage • Explanation: Benchmark developers should clearly define the scope of the medical specialties of the benchmark, such as clinical departments, disease types, or task types. • Justification: By clearly defining the medical scope, it helps users better un- derstand the breath and depth of the cov- erage of the benchmark. It also help...

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    • Justification: An effective benchmark should serve the needs of users

    Demonstration of User Needs • Explanation: The benchmark should re- flect the core concerns and assessment needs of its target users, such as address- ing specific clinical challenges or over- coming technical obstacles. • Justification: An effective benchmark should serve the needs of users. Evi- dence of user needs justifies the devel- opment of the ben...

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    • Justification: Due to the professionalism and rigor required in the medical field, the development of a benchmark must involve deep engagement from domain experts

    Domain Experts Involvement • Explanation: Domain expert (e.g., physicians or clinical researchers) should be involved in the development of the benchmark. • Justification: Due to the professionalism and rigor required in the medical field, the development of a benchmark must involve deep engagement from domain experts. Their expertise are fundamen- tal to...

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    • Justification: Benchmark content should adhere to recognized, evidence- based medical knowledge sources

    Authoritative Knowledge Sources • Explanation: Benchmark developers must clearly specify which authorita- tive medical knowledge sources (e.g., clinical guidelines, textbooks, medical databases) the benchmark content is based on. • Justification: Benchmark content should adhere to recognized, evidence- based medical knowledge sources. By using authoritati...

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    • Justification: Adherence to medical standards ensures the clinical relevance and consistency, facilitating integration and comparison in reflect real-world medical practice

    Medical Standards Alignment • Explanation: Benchmarks should follow internationally or industry- recognized medical standards (e.g., ICD, SNOMED CT, LOINC) when medical terminology is involved. • Justification: Adherence to medical standards ensures the clinical relevance and consistency, facilitating integration and comparison in reflect real-world medic...

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    • Justification: Evaluation metrics di- rectly shape the interpretation of results

    Validity of Core Metric • Explanation: The core performance metric should be clearly defined and closely related to the clinical task or med- ical capability being assessed. • Justification: Evaluation metrics di- rectly shape the interpretation of results. Choosing suitable metric and explaining its relevance ensures a shared understand- ing and comprehe...

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    • Justification: In high-stakes medical do- main, going beyond correctness is vi- tal

    Multi-dimensional Evaluation 27 • Explanation: Apart from the correct- ness, benchmark developers should in- clude evaluation of other important di- mensions (e.g., safety, fluency). • Justification: In high-stakes medical do- main, going beyond correctness is vi- tal. Multi-dimensional evaluation of- fers a more comprehensive assessment of whether a mode...

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    Safety and Fairness Considerations • Explanation: Benchmark should in- clude evaluation for potential risks (e.g., unsafe recommendation, toxicity) and bias (e.g., gender, ethnicity) in model outputs. • Justification: Evaluating risks and fair- ness facilitates bias-free, safe and equi- table applications of LLMs in the med- ical domain, promoting respons...

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    • Justification: Clear and traceable data sources are critical to ensure trans- parency and ethical data usage, which is especially important when sensitive data is involved

    Data source transparency and traceability • Explanation: Benchmark developers should clearly state the data source of the benchmark, along with relevant trace- ability information (e.g., data collection time frame, platforms). • Justification: Clear and traceable data sources are critical to ensure trans- parency and ethical data usage, which is especiall...

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    • Justification: Collecting data from unre- liable sources may lead to invalid results for medical applications

    Data Source Reliability • Explanation: Benchmark developers should clearly describe the selection cri- teria and explain the reliability of the data source. • Justification: Collecting data from unre- liable sources may lead to invalid results for medical applications. By explaining the reliability of the data source, credibil- ity of the benchmark can be...

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  26. [26]

    For synthetically generated data, the con- struction process and verification for its authenticity (e.g., expert review) should also be described

    Data Authenticity • Explanation: Benchmark developers should clearly specify whether the data comes from the real world scenarios, syn- thetically generated, or a mixture of both. For synthetically generated data, the con- struction process and verification for its authenticity (e.g., expert review) should also be described. • Justification: Real-world da...

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    • Justification: A benchmark that lacks representativeness may lead to bias in evaluation, reducing the clinical rele- vance, generalizability and fairness of the results

    Dataset Representativeness • Explanation: The representativeness of key features (e.g., patient age, disease) of the dataset should be explained and statistically analyzed. • Justification: A benchmark that lacks representativeness may lead to bias in evaluation, reducing the clinical rele- vance, generalizability and fairness of the results. • Scoring: –...

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    • Justification: Ensuring the dataset cov- ers a variety helps comprehensively eval- uate the model’s generalization ability, reducing bias in the evaluation results

    Dataset Diversity • Explanation: The benchmark should have a diverse coverage, and provide quantitative evidence of its diversity of disease or medical departments covered. • Justification: Ensuring the dataset cov- ers a variety helps comprehensively eval- uate the model’s generalization ability, reducing bias in the evaluation results. • Scoring: – 0: D...

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    • Justification: It ensures that the final dataset is well-structured, enhancing re- liability

    Data Cleaning and Standardization • Explanation: The processes and steps of data preprocessing, including data cleaning and standardization, should be clearly described. • Justification: It ensures that the final dataset is well-structured, enhancing re- liability. Also, it allows a better under- standing of the construction process of the benchmark, ensu...

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    Methods of de-identification should be described and compliance with relevant regulations (e.g., HIPAA) should be clearly stated

    Privacy Protection • Explanation: If sensitive data is used, it should be de-identified. Methods of de-identification should be described and compliance with relevant regulations (e.g., HIPAA) should be clearly stated. • Justification: Real-world clinical data may contain patient information. It is es- sential to ensure that the data and privacy protectio...

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    • Justification: A clear and consistent for- mat is essential for standardized evalua- tion

    Data Format Clarity and Consistency 29 • Explanation: The data in the dataset, including questions, cases, or task de- scriptions, should be written clearly and presented in a consistent format. • Justification: A clear and consistent for- mat is essential for standardized evalua- tion. Inconsistent format may affect mod- els’ interpretation, compromising...

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    • Justification: A dataset construction pro- cess without review mechanism is prone to errors

    Data Review and Audit • Explanation: The dataset construction process should include a review proce- dure with involvement of medical ex- perts. • Justification: A dataset construction pro- cess without review mechanism is prone to errors. Careful review that involves medical experts can ensures clinical rel- evance, professionalism and reliable of the da...

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    • Justification: Clear reference answers or scoring guidelines ensures transpar- ent and accurate evaluation

    Quality of Reference Answer • Explanation: The benchmark should provide clear and accurate reference an- swers or scoring guidelines, and explain the construction and verification process (e.g., expert consensus). • Justification: Clear reference answers or scoring guidelines ensures transpar- ent and accurate evaluation. By explain- ing how are reference...

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    • Justification: Data contamination may lead to inflated performance, which only reflect memorization instead of medical capability from the models

    Data Contamination Prevention • Explanation: Benchmark developers should take actions to identify, address and prevent potential data contamination issues of the data. • Justification: Data contamination may lead to inflated performance, which only reflect memorization instead of medical capability from the models. Preventing and controlling potential con...

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    • Justification: It ensures that users can use the benchmark conveniently, promot- ing benchmark adoption and ensuring fair, transparent, and consistent evalua- tion

    User-friendliness of evaluation tools • Explanation: Evaluation scripts or tools that is easy to obtain and use should be provided. • Justification: It ensures that users can use the benchmark conveniently, promot- ing benchmark adoption and ensuring fair, transparent, and consistent evalua- tion. • Scoring: – 0: No evaluation scripts or tools is provided...

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    Technical Reproducibility • Explanation: Tools and technical doc- umentation (e.g., environment configu- ration, dependency versions) should be provided. • Justification: The availability of evalua- tion tools and clear technical documenta- tion ensures reproducibility of reported results, thereby enhancing the credibility of the benchmark. • Scoring: – 0...

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    • Justification: Providing different per- formance baselines allows comparison against the model’s performance, en- abling a deeper understanding and better interpretability

    Provision of performance baselines • Explanation: Benchmark developers should provide multiple meaningful per- formance baselines, such as random, baseline models and human perfor- mance. • Justification: Providing different per- formance baselines allows comparison against the model’s performance, en- abling a deeper understanding and better interpretabi...

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    • Justification: In medical domain, un- derstanding the model’s decision-making process is just as important as the final answer

    Reasoning Process Evaluation • Explanation: Apart from the final an- swer, the benchmark includes evalua- tions for the model’s reasoning process or explanation abilities. • Justification: In medical domain, un- derstanding the model’s decision-making process is just as important as the final answer. Evaluating the reasoning process helps ensure that the ...

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    Robustness testing en- sures model’s output is consistent and reliable under different conditions

    Robustness Evaluation • Explanation: Benchmark should in- clude testing for the model’s stability and robustness (e.g., input perturbations, ad- versarial samples) • Justification: In practical applications, models may encounter different varia- tions of inputs. Robustness testing en- sures model’s output is consistent and reliable under different conditi...

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    Generalization Capability Evaluation • Explanation: The benchmark design should help evaluate the generalization capability of models to unseen data or scenarios (e.g., careful train/test split, out-of-distribution testing). 31 • Justification: Due to the high variability in medical scenarios, assessing a model’s generalization capability is essential for...

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    I don’t know

    Uncertainty Evaluation • Explanation: Benchmark should in- clude evaluation for the model’s ability to recognize and express its own uncer- tainty (e.g., responding “I don’t know”) • Justification: Incorrect overconfident an- swers can be dangerous in high-stakes medical applications. A model’s ability to accurately identify and express uncer- tainty is c...

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    • Justification: It ensures that different types of models can be tested under the same interface

    Evaluation Flexibility • Explanation: Evaluation code should have modular interface and support dif- ferent models, including closed-source models that require API access and local open-source models. • Justification: It ensures that different types of models can be tested under the same interface. • Scoring: – 0: Supports only one type of model and is di...

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    • Justification: Sufficient coverage of the core medical competencies the bench- mark aims to measure is the prerequisite for establishing content validity

    Knowledge and Skill Coverage • Explanation: Evidence (e.g., expert evaluation) is provided to demonstrate that the benchmark content sufficiently covers the medical knowledge and skills it claims to assess. • Justification: Sufficient coverage of the core medical competencies the bench- mark aims to measure is the prerequisite for establishing content val...

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    • Justification: Ensuring that the evalu- ation task closely mirrors the targeted clinical practice in real-world scenarios enhance the relevance of the benchmark

    Scenario Authenticity • Explanation: The evaluation tasks of the benchmark should effectively simulate and mirror real-world medical scenario. • Justification: Ensuring that the evalu- ation task closely mirrors the targeted clinical practice in real-world scenarios enhance the relevance of the benchmark. It helps assess whether the model can be transferr...

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    • Justification: An effective benchmark should be capable of differentiating and distinguishing models of varying capabil- ities

    Model Discrimination Ability • Explanation: Benchmark developers should provide experiment data and anal- ysis, indicating that the benchmark can effectively distinguish the capability be- tween different models. • Justification: An effective benchmark should be capable of differentiating and distinguishing models of varying capabil- ities. It provides me...

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    Correlation with Clinical Performance • Explanation: Benchmark developers should provide evidence that prelimi- narily explores the correlation between benchmark performance and the model’s performance in actual clinical applica- tions. • Justification: Validating whether bench- mark results have meaningful indica- tion on the model’s performance in real-...

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    • Scoring: – 0: Does not mention or conduct any internal consistency measurement

    Internal Consistency • Explanation: For different sections or items of the benchmark evaluating the same capability, benchmark developers should demonstrate good internal consis- tency (e.g., Cronbach’sα) • Justification: It ensures that different sections or items reliably assess the same capability, enabling meaningful compar- isons and interpretation o...

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    Statistical Significance Reporting • Explanation: When comparing the per- formance of different models, statistical significance (e.g., confidence intervals, and p-values) should be report. • Justification: Statistical significance testing allows for a more informed inter- pretation of results by differentiating the performance differences between mod- el...

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    • Justification: A complete and clear doc- umentation help users understand and use the benchmark properly, enhancing the usability, reproducibility and trans- parency

    Documentation Completeness • Explanation: Benchmark developers should provide a clear and comprehen- sive documentation of the benchmark, systematically describing the relevant de- tails, including the design, objectives, scope, construction process, task defini- tions and evaluation procedures. • Justification: A complete and clear doc- umentation help u...

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    • Justification: Clear evaluation guide- lines help users better understand how model performance is quantified, ensur- ing a shared interpretation and under- standing

    Clarity of Evaluation Guidelines • Explanation: Benchmark developers should provide evaluation guidelines, with definitions of evaluation metrics, de- tailed scoring criteria, and easy-to-follow usage instructions for users to replicate the evaluation process. • Justification: Clear evaluation guide- lines help users better understand how model performanc...

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    • Justification: Disclosing limitations and risks demonstrates scientific rigor and responsibility

    Discussion of Limitations and Risks • Explanation: The benchmark develop- ers should openly discuss the limitations and potential social risks of the bench- mark. • Justification: Disclosing limitations and risks demonstrates scientific rigor and responsibility. This transparency helps prevent users from misunderstanding and misusing the benchmark, especi...

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    • Justification: Going through peer review process means that the design, validity and results of a benchmark has been rig- orous evaluated

    Peer Review • Explanation: The benchmark and its corresponding paper was accepted at peer-reviewed venue. • Justification: Going through peer review process means that the design, validity and results of a benchmark has been rig- orous evaluated. It enhances credibility and ensures quality. • Scoring: – 0: The benchmark has not been ac- cepted at a peer-r...

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    on platforms like GitHub or Hugging Face) along with the applica- ble license

    Accessibility of Evaluation Code and Data • Explanation: Access to the evaluation code and data, which can be shared within legal and ethical boundaries, is provided (e.g. on platforms like GitHub or Hugging Face) along with the applica- ble license. • Justification: Accessible code and data are prerequisites for reproducibility. Moreover, it allows the c...

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    • Justification: Proper usage and citation guidelines help maintain academic in- tegrity

    Usage and Citation Guidelines • Explanation: Clear guidelines are pro- vided to standardize benchmark usage, result reporting, and correct citation for- mats in academic papers or technical re- ports. • Justification: Proper usage and citation guidelines help maintain academic in- tegrity. It also promote standardized re- porting of results, facilitating ...

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    • Justification: Maintaining an effective feedback channel allows users to provide feedback when issues with the bench- mark are discovered

    Update and Version Management • Explanation: A feedback channel should be maintained for users to report problems, provide feedback and sugges- tions. • Justification: Maintaining an effective feedback channel allows users to provide feedback when issues with the bench- mark are discovered. This is crucial for continuously improving benchmark qual- ity an...

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    • Justification: Maintaining an effective feedback channel allows users to provide feedback when issues with the bench- mark are discovered

    Feedback Channel for Users • Explanation: A feedback channel should be maintained for users to report problems, provide feedback and sugges- tions. • Justification: Maintaining an effective feedback channel allows users to provide feedback when issues with the bench- mark are discovered. This is crucial for continuously improving benchmark qual- ity and f...

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    • Justification: Clarifying who holds long- term responsibility reassures the commu- nity that it will be actively supported and improved, ensuring usability and credi- bility

    Long-term Maintenance Responsibility • Explanation: The individuals, teams, or institutions responsible for its long-term maintenance and development should be clearly stated. • Justification: Clarifying who holds long- term responsibility reassures the commu- nity that it will be actively supported and improved, ensuring usability and credi- bility. • Sc...

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