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arxiv: 2503.17599 · v3 · pith:XLP6RMQVnew · submitted 2025-03-22 · 💻 cs.CL · cs.AI

Evaluating Clinical Competencies of Large Language Models with a General Practice Benchmark

Zheqing Li , Yiying Yang , Jiping Lang , Wenhao Jiang , Junrong Chen , Yuhang Zhao , Shuang Li , Dingqian Wang
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Zhu Lin Xuanna Li Yuze Tang Jiexian Qiu Xiaolin Lu Hongji Yu Shuang Chen Yuhua Bi Xiaofei Zeng Yixian Chen Lin Yao
This is my paper

Pith reviewed 2026-05-22 23:38 UTC · model grok-4.3

classification 💻 cs.CL cs.AI
keywords large language modelsgeneral practiceclinical benchmarkLLM evaluationmedical AIcompetency assessmentprimary care
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The pith

Large language models are not suitable for autonomous deployment in general practice based on a new expert-annotated benchmark.

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

The paper creates GPBench to test LLMs against the actual daily responsibilities of general practitioners rather than simplified exam questions. Ten state-of-the-art models were evaluated using data annotated by domain experts to match routine clinical standards. The results show consistent gaps in performance that prevent independent operation. This finding indicates that realistic clinical use of these models requires ongoing human oversight and that further model optimization focused on GP tasks is still needed.

Core claim

The paper establishes that current LLMs are not suitable for autonomous deployment in clinical general practice. All realistic applications require continuous human oversight, and further optimization specifically tailored to the daily responsibilities of GPs remains essential. The conclusion rests on evaluations conducted with GPBench, a benchmark whose data are meticulously annotated by domain experts in accordance with routine clinical practice standards.

What carries the argument

GPBench, a general practice benchmark whose data are meticulously annotated by domain experts in accordance with routine clinical practice standards, used as the evaluation framework to measure LLM competencies against GP duties.

If this is right

  • Current LLMs cannot reliably fulfill the duties of general practitioners.
  • All realistic applications of LLMs in general practice require continuous human oversight.
  • Further optimization specifically tailored to the daily responsibilities of GPs remains essential.

Where Pith is reading between the lines

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

  • GPBench scores could serve as a baseline for measuring progress in future LLM versions aimed at primary care.
  • Similar expert-annotated benchmarks may prove useful for evaluating LLMs in other medical specialties.
  • Practical deployment strategies could combine LLMs with structured human review processes calibrated to GPBench performance levels.

Load-bearing premise

The GPBench framework, constructed from expert annotations aligned with routine clinical practice standards, accurately measures the competencies needed for autonomous general practitioner duties.

What would settle it

A demonstration that an LLM reaches human GP performance levels on GPBench tasks during real clinical deployment with no ongoing supervision would challenge the central claim.

read the original abstract

Large Language Models (LLMs) have demonstrated considerable potential in general practice. However, existing benchmarks and evaluation frameworks primarily depend on exam-style or simplified question-answer formats, lacking a competency-based structure aligned with the real-world clinical responsibilities encountered in general practice. Consequently, the extent to which LLMs can reliably fulfill the duties of general practitioners (GPs) remains uncertain. In this work, we propose a novel evaluation framework to assess the capability of LLMs to function as GPs. Based on this framework, we introduce a general practice benchmark (GPBench), whose data are meticulously annotated by domain experts in accordance with routine clinical practice standards. We evaluate ten state-of-the-art LLMs and analyze their competencies. Our findings indicate that current LLMs are not suitable for autonomous deployment in clinical general practice and that all realistic applications require continuous human oversight; further optimization specifically tailored to the daily responsibilities of GPs remains essential.

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

4 major / 1 minor

Summary. The paper introduces GPBench, a competency-based evaluation framework and benchmark for LLMs in general practice, constructed from expert-annotated cases aligned with routine clinical practice standards. It evaluates ten state-of-the-art LLMs on this benchmark and concludes that current LLMs are not suitable for autonomous deployment in clinical general practice, that all realistic applications require continuous human oversight, and that further optimization tailored to GP daily responsibilities remains essential.

Significance. The introduction of a competency-based framework that moves beyond exam-style or simplified QA formats to align with real-world GP responsibilities is a clear strength and addresses a documented gap in existing medical AI benchmarks. If the GPBench items are shown to be reliable and if performance on them correlates with clinical safety outcomes, the results would provide actionable evidence on the current limitations of LLMs for high-stakes autonomous use and reinforce the importance of human-in-the-loop designs in clinical AI.

major comments (4)
  1. [Abstract] Abstract: the central claim that LLMs are unsuitable for autonomous deployment is asserted without any reported model scores, inter-annotator agreement, benchmark validation steps, or error analysis, preventing assessment of whether the evidence supports the conclusion.
  2. [GPBench construction] GPBench construction: the expert annotation process aligned with routine clinical standards is described, but no inter-annotator reliability statistics are supplied, leaving the consistency and validity of the benchmark data unverified.
  3. [Evaluation results] Evaluation results: no licensed-GP baseline scores on the identical GPBench items are reported, so it is impossible to distinguish whether low LLM performance reflects model inadequacy or benchmark difficulty.
  4. [Discussion] Discussion: the inference that continuous human oversight is required for all realistic applications rests on benchmark scores alone, without any mapping from observed errors to documented adverse events or safety data from primary care.
minor comments (1)
  1. [Abstract] The abstract would benefit from inclusion of the key quantitative LLM performance figures to allow readers to gauge the magnitude of the reported gaps.

Simulated Author's Rebuttal

4 responses · 0 unresolved

We thank the referee for the thoughtful and constructive feedback, which highlights important areas for strengthening the manuscript's claims and transparency. We address each major comment point-by-point below, proposing revisions where the points identify verifiable gaps in the current version.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central claim that LLMs are unsuitable for autonomous deployment is asserted without any reported model scores, inter-annotator agreement, benchmark validation steps, or error analysis, preventing assessment of whether the evidence supports the conclusion.

    Authors: We agree that the abstract would benefit from greater specificity to allow readers to evaluate the central claim. In the revised version, we will expand the abstract to include summary performance metrics across the ten LLMs (e.g., overall competency scores and key failure rates), a brief reference to the expert annotation process and inter-annotator agreement, and mention of the error analysis presented in the results section. This will make the evidence supporting the conclusion more immediately assessable while remaining within abstract length constraints. revision: yes

  2. Referee: [GPBench construction] GPBench construction: the expert annotation process aligned with routine clinical standards is described, but no inter-annotator reliability statistics are supplied, leaving the consistency and validity of the benchmark data unverified.

    Authors: This is a valid observation. While the annotation followed a multi-expert process with standardized clinical guidelines, inter-annotator agreement statistics were not reported in the original submission. We will add these metrics (e.g., Cohen's kappa or percentage agreement) to the methods section of the revised manuscript, calculated on a subset of overlapping annotations, to provide quantitative evidence of benchmark reliability. revision: yes

  3. Referee: [Evaluation results] Evaluation results: no licensed-GP baseline scores on the identical GPBench items are reported, so it is impossible to distinguish whether low LLM performance reflects model inadequacy or benchmark difficulty.

    Authors: We acknowledge the value of a direct human baseline for calibration. The GPBench items were constructed from routine clinical practice standards that licensed GPs are expected to meet, providing an implicit reference point. However, administering the full benchmark to a cohort of licensed GPs would require substantial additional resources, recruitment, and ethics approvals not feasible within the current study timeline. In the revision, we will explicitly discuss this limitation, provide qualitative context on expected GP performance based on the competency framework, and recommend human baseline collection as important future work. revision: partial

  4. Referee: [Discussion] Discussion: the inference that continuous human oversight is required for all realistic applications rests on benchmark scores alone, without any mapping from observed errors to documented adverse events or safety data from primary care.

    Authors: We agree that stronger linkage to real-world safety outcomes would reinforce the practical implications. The current inference draws from the benchmark's alignment with documented GP competencies, where failures in areas such as diagnosis, management, or communication carry inherent clinical risks. We will revise the discussion to more explicitly acknowledge the absence of direct adverse-event mapping, clarify that the recommendation for human oversight is based on competency gaps rather than proven harm, and highlight the need for future studies correlating GPBench performance with primary-care safety data. revision: yes

Circularity Check

0 steps flagged

No circularity: new expert-annotated benchmark with no equations, fits, or self-referential derivations

full rationale

The paper constructs GPBench from fresh expert annotations aligned to routine clinical standards and evaluates LLMs on it. No equations, parameters, or predictions are fitted to subsets of the data; the central claim (LLMs unsuitable for autonomous use) follows directly from observed scores on the new benchmark rather than reducing to any self-definition, self-citation chain, or renamed known result. The derivation chain is therefore self-contained against external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The evaluation framework depends on the assumption that expert annotations faithfully represent real GP competencies; no free parameters or new entities are introduced.

axioms (1)
  • domain assumption Domain expert annotations accurately reflect routine clinical practice standards for general practitioners.
    Benchmark data are meticulously annotated by domain experts in accordance with routine clinical practice standards.

pith-pipeline@v0.9.0 · 5749 in / 925 out tokens · 39126 ms · 2026-05-22T23:38:03.903457+00:00 · methodology

discussion (0)

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Reference graph

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    work page