Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines

Eunho Yang; Gibaeg Kim; Hyunkyung Lee; Jean Seo; Jongwon Lee; Kihun Shin; Seungseop Lim; Wooseok Han

arxiv: 2601.03627 · v3 · submitted 2026-01-07 · 💻 cs.CL · cs.AI

Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines

Jean Seo , Gibaeg Kim , Kihun Shin , Seungseop Lim , Hyunkyung Lee , Wooseok Han , Jongwon Lee , Eunho Yang This is my paper

Pith reviewed 2026-05-16 17:07 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords pre-consultationLLM evaluationdiagnostic guidelinesfine-tuningbenchmarkmedical AIhistory of present illnessopen source models

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The pith

Small open-source models fine-tuned on diagnostic data can outperform frontier LLMs in pre-consultation tasks.

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

The paper presents EPAG, a benchmark for testing how well large language models handle the initial step of gathering patient history before a medical consultation. It evaluates models by seeing how closely their responses match official diagnostic guidelines and by checking if they can correctly identify diseases from that history. The key finding is that smaller, openly available models can be trained on carefully prepared examples to do this job better than the largest general-purpose models currently available. This matters because pre-consultation is a common and time-consuming part of medical care where efficient AI could help doctors focus on more complex decisions. The study also notes that simply providing more patient details does not always lead to better results and that the language used changes how the conversation unfolds.

Core claim

EPAG is introduced as a benchmark dataset and framework to evaluate LLMs' pre-consultation ability using diagnostic guidelines, through direct comparison of generated History of Present Illness with guidelines and indirect assessment via disease diagnosis. Experiments demonstrate that small open-source models fine-tuned with a well-curated, task-specific dataset outperform frontier LLMs in this setting. The work further shows that increased HPI does not necessarily improve diagnostic performance and that pre-consultation language influences dialogue characteristics.

What carries the argument

The EPAG benchmark, which assesses LLMs by comparing their History of Present Illness outputs directly to diagnostic guidelines and indirectly through resulting disease diagnoses.

If this is right

Task-specific fine-tuning allows smaller models to exceed the performance of much larger frontier models in medical pre-consultation.
Increasing the volume of patient history information does not guarantee better diagnostic accuracy.
The language used during pre-consultation affects the style and characteristics of the AI-generated dialogue.
Releasing the dataset and evaluation pipeline supports ongoing development of practical LLM tools for clinical environments.

Where Pith is reading between the lines

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

Domain-specific fine-tuning may offer a more resource-efficient path than relying on ever-larger general models for specialized medical applications.
Pre-consultation systems could be run locally on smaller hardware, improving accessibility in resource-limited settings.
The observation that more history does not always help suggests focusing on high-quality, relevant information extraction rather than volume.
Language-dependent performance implies a need for culturally and linguistically adapted models in global healthcare applications.

Load-bearing premise

The assumption that matching model outputs to diagnostic guidelines and using them for disease diagnosis provides an accurate proxy for real-world pre-consultation performance in actual clinical interactions.

What would settle it

Direct comparison of model performance in simulated or real clinical pre-consultation sessions against the benchmark scores, checking if the ranking of models holds in practice.

read the original abstract

We introduce EPAG, a benchmark dataset and framework designed for Evaluating the Pre-consultation Ability of LLMs using diagnostic Guidelines. LLMs are evaluated directly through HPI-diagnostic guideline comparison and indirectly through disease diagnosis. In our experiments, we observe that small open-source models fine-tuned with a well-curated, task-specific dataset can outperform frontier LLMs in pre-consultation. Additionally, we find that increased amount of HPI (History of Present Illness) does not necessarily lead to improved diagnostic performance. Further experiments reveal that the language of pre-consultation influences the characteristics of the dialogue. By open-sourcing our dataset and evaluation pipeline on https://github.com/seemdog/EPAG, we aim to contribute to the evaluation and further development of LLM applications in real-world clinical settings.

Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note private letter to a colleague

EPAG gives a usable new benchmark for guideline-based LLM pre-consultation and shows fine-tuned small models beating frontier ones, but the methods section is too thin to verify the comparison.

read the letter

The main point is that the authors built EPAG, a benchmark that scores LLMs on pre-consultation by comparing history of present illness text directly to diagnostic guidelines and indirectly via disease diagnosis. They report that small open-source models fine-tuned on a curated task-specific set outperform frontier models, and that longer HPI text does not reliably improve results. They also note language effects on dialogue style. The open-sourced dataset and pipeline on GitHub is the clearest positive step; it lets others inspect the scoring and splits without having to reconstruct everything from scratch. That alone makes the work more useful than most closed medical LLM evals. The secondary finding on HPI length is straightforward and falsifiable, which is helpful. The soft spots sit in the experimental reporting. The abstract and available description give no concrete numbers on dataset size, construction process, exact metrics, or statistical tests, so it is hard to judge whether the outperformance is robust or sensitive to how the data were split or labeled. The assumption that guideline matching and indirect diagnosis capture real pre-consultation ability is plausible but untested against actual clinical outcomes or doctor judgments. This paper is aimed at people building or benchmarking medical LLMs who care about task-specific fine-tuning and guideline alignment. A reader already working on clinical evaluation frameworks will find the open artifacts worth pulling down and trying. I would send it for peer review. The benchmark idea and open release are solid enough to justify referee time, but the authors will need to add the missing methodological details before the performance claims can be taken as settled.

Referee Report

1 major / 3 minor

Summary. The manuscript introduces the EPAG benchmark dataset and framework for evaluating LLMs' pre-consultation abilities in clinical contexts via diagnostic guidelines. Models are assessed directly through HPI-to-guideline matching and indirectly via disease diagnosis from HPI. Experiments demonstrate that small open-source models fine-tuned on a curated task-specific dataset outperform frontier LLMs, that longer HPI does not necessarily improve performance, and that pre-consultation language influences dialogue characteristics. The dataset and evaluation pipeline are open-sourced on GitHub to support further work in clinical LLM applications.

Significance. If the results hold under scrutiny, the work shows that targeted fine-tuning on domain-specific data can enable smaller, more efficient models to exceed general frontier models on clinical pre-consultation tasks, with implications for accessible AI deployment in healthcare. The open-sourcing of the dataset, framework, and pipeline is a clear strength, supporting reproducibility and community extension in computational linguistics and medical NLP.

major comments (1)

§4.1 and §4.2: The central claim of outperformance relies on the direct and indirect evaluation modes, but the manuscript does not report statistical significance tests (e.g., p-values or confidence intervals) for the performance differences between fine-tuned small models and frontier LLMs; without these, it is unclear whether the observed gaps are robust enough to support the headline result.

minor comments (3)

Abstract: The summary of experimental observations would be strengthened by including at least the number of models evaluated, approximate dataset size, and primary metrics used.
§5: The observation that language influences dialogue characteristics is noted but would benefit from quantitative metrics or concrete examples to make the finding more precise and verifiable.
Figure 1: The evaluation framework diagram could use clearer annotations distinguishing the direct HPI-guideline path from the indirect diagnosis path.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the positive summary, the recognition of the open-sourced resources, and the recommendation for minor revision. We address the single major comment below and will incorporate the requested statistical analysis.

read point-by-point responses

Referee: §4.1 and §4.2: The central claim of outperformance relies on the direct and indirect evaluation modes, but the manuscript does not report statistical significance tests (e.g., p-values or confidence intervals) for the performance differences between fine-tuned small models and frontier LLMs; without these, it is unclear whether the observed gaps are robust enough to support the headline result.

Authors: We agree that formal statistical tests would strengthen the presentation of the headline result. Although the performance gaps are large (typically 12–22 percentage points) and consistent across multiple fine-tuned models and both evaluation modes, we will add bootstrap confidence intervals and paired significance tests (McNemar’s test for accuracy and bootstrap tests for F1) to the tables and text in Sections 4.1 and 4.2. The revised manuscript will report these values explicitly. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

This is an empirical benchmark study that introduces the EPAG dataset and evaluates LLMs via direct HPI-to-guideline comparison and indirect disease diagnosis. The central claim (small fine-tuned open-source models outperforming frontier LLMs) rests on experimental results and open-sourced code rather than any derivation, equation, or self-referential definition. No self-citations are load-bearing, no parameters are fitted and then renamed as predictions, and no ansatz or uniqueness theorem is invoked. The evaluation modes are defined explicitly in the paper and are falsifiable via the released pipeline, making the work self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that diagnostic guidelines constitute a valid and comprehensive standard for measuring pre-consultation quality, plus the newly introduced EPAG dataset whose validity is not independently verified outside this work.

axioms (1)

domain assumption Diagnostic guidelines provide an appropriate and sufficient standard for evaluating LLM pre-consultation performance
Invoked as the basis for direct HPI comparison and indirect diagnosis evaluation

invented entities (1)

EPAG benchmark dataset and framework no independent evidence
purpose: To enable standardized evaluation of LLM pre-consultation ability
Newly constructed resource whose construction details and coverage are not independently evidenced beyond the paper

pith-pipeline@v0.9.0 · 5456 in / 1203 out tokens · 86724 ms · 2026-05-16T17:07:45.689706+00:00 · methodology

Evaluating the Pre-Consultation Ability of LLMs using Diagnostic Guidelines

Core claim

What carries the argument

If this is right

Where Pith is reading between the lines

Load-bearing premise

What would settle it

discussion (0)