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arxiv: 2605.09132 · v1 · submitted 2026-05-09 · 💻 cs.CV

Recognition: 2 theorem links

· Lean Theorem

KEPIL: Knowledge-Enhanced Prompt-Image Learning for Prompt-Robust Disease Detection

Haozhe Luo , Shelley Zixin Shu , Ziyu Zhou , Robert Berke , Mauricio Reyes
Authors on Pith no claims yet

Pith reviewed 2026-05-12 03:06 UTC · model grok-4.3

classification 💻 cs.CV
keywords vision-language modelszero-shot learningprompt robustnessradiologymedical imagingknowledge integrationcontrastive learningdisease detection
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The pith

A framework that enriches radiology prompts with medical ontologies and aligns prompt variants during training stabilizes zero-shot disease detection in vision-language models.

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

The paper presents KEPIL as a way to make medical vision-language models less sensitive to how prompts are phrased when performing zero-shot inference on radiology images. It does this by pulling in structured knowledge from ontologies with LLM help, using a contrastive loss that treats different wordings of the same finding as equivalent, and standardizing reports around ontology entities. A reader would care because current models falter on rare conditions and on the varied language doctors actually use, which limits safe clinical use. If the approach holds, it points toward VLMs that can reason reliably over images and text without constant prompt tuning.

Core claim

KEPIL integrates curated medical knowledge to stabilize zero-shot generalization in radiology VLMs through three components: dynamic prompt enrichment from ontologies with LLM assistance, a semantic-aware contrastive loss that aligns embeddings of equivalent prompt variants via a dual-embedding objective, and entity-centric report standardization for ontology-aligned representations. Across seven benchmarks this yields state-of-the-art zero-shot performance, and under explicit prompt-variation tests it raises AUC by 6.37 percent on CheXpert and 4.11 percent on average.

What carries the argument

The KEPIL framework, which combines ontology-driven prompt enrichment, a dual-embedding contrastive loss to treat prompt variants as equivalent, and entity-centric report standardization to produce robust cross-modal embeddings.

If this is right

  • Zero-shot detection becomes viable for long-tailed radiological findings that lack labeled examples.
  • Clinical users can employ varied natural-language prompts without large drops in model reliability.
  • Joint image-text reasoning in VLMs improves when external structured knowledge is injected at inference time.
  • Report standardization around ontology terms yields more consistent embeddings across different writing styles.
  • Overall AUC gains of several percent under prompt variation translate to more trustworthy outputs on standard benchmarks.

Where Pith is reading between the lines

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

  • The same knowledge-injection pattern could be tested on non-radiology modalities such as pathology slides or ultrasound to check domain transfer.
  • If LLM assistance in knowledge curation introduces systematic inaccuracies, performance might degrade on edge-case presentations not represented in the source ontologies.
  • Deploying the model in live clinical workflows would reveal whether the robustness holds when prompts come from actual physicians rather than benchmark variations.
  • Comparing KEPIL-style knowledge enrichment against purely data-augmentation methods for prompt robustness could isolate the contribution of the external ontology source.

Load-bearing premise

Curated medical knowledge drawn from ontologies and refined by LLMs will be sufficiently accurate and unbiased to improve generalization without introducing new errors.

What would settle it

Measuring zero-shot AUC on a held-out collection of prompt rephrasings for a rare disease whose ontology entries were not used in enrichment, and finding no gain or a drop relative to a plain CLIP-style baseline.

Figures

Figures reproduced from arXiv: 2605.09132 by Haozhe Luo, Mauricio Reyes, Robert Berke, Shelley Zixin Shu, Ziyu Zhou.

Figure 1
Figure 1. Figure 1: Overview of the proposed KEPIL framework. The model takes three in [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 3
Figure 3. Figure 3: Evaluation of the impact of prompts from ChatGPT-4o, Grok-3, and Claude Sonnet 4 (including syntactic paraphrases, typos, omissions, punctuation variants) on chest X-ray model performance relative to the original training prompt. KEPIL demonstrates smaller performance declines. compare our Lsc with naive text augmentation. our Lsc objective stays strictly within the clinical language manifold and enforces … view at source ↗
Figure 4
Figure 4. Figure 4: KEPIL’s performance across incremental different inference input types. Performance improves with increasingly rich textual input, highlighting the value of more informative prompts [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
read the original abstract

Vision--language models (VLMs) show promise for clinical decision support in radiology because they enable joint reasoning over radiological images and clinical text, thereby leveraging complementary clinical information. However, radiological findings are long-tailed in practice, leaving some conditions underrepresented and making zero-shot inference essential. Yet current CLIP-style medical VLMs are sensitive to prompt variations and often lack trustworthy external knowledge at inference time, which hinders reliable clinical deployment. We present \textit{KEPIL}, a prompt-robust framework that integrates curated medical knowledge to stabilize zero-shot generalization. KEPIL comprises: (i) \emph{dynamic prompt enrichment} using ontologies with LLM assistance, (ii) a \emph{semantic-aware contrastive loss} aligning embeddings of equivalent prompt variants via a dual-embedding objective, and (iii) \emph{entity-centric report standardization} to yield ontology-aligned representations. Across seven benchmarks, KEPIL achieves state-of-the-art zero-shot inference performance; under prompt-variation tests, it improves AUC by \(6.37\%\) on \textit{CheXpert} and by \(4.11\%\) on average. These results suggest that structured knowledge and robust prompt design are key to clinically reliable radiology-facing VLMs. Code will be released at https://github.com/Roypic/KEPIL.

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 / 2 minor

Summary. The paper proposes KEPIL, a framework for prompt-robust zero-shot disease detection in radiology VLMs. It integrates (i) dynamic prompt enrichment combining medical ontologies with LLM assistance, (ii) a semantic-aware contrastive loss that aligns embeddings of equivalent prompt variants through a dual-embedding objective, and (iii) entity-centric report standardization to produce ontology-aligned representations. The central empirical claims are state-of-the-art zero-shot performance across seven benchmarks and improved robustness under prompt variation, specifically +6.37% AUC on CheXpert and +4.11% on average.

Significance. If the reported gains prove robust, the work would meaningfully advance reliable clinical use of VLMs by addressing prompt sensitivity and the absence of external knowledge at inference time, particularly for long-tailed radiological findings. The planned code release supports reproducibility and allows independent verification of the contrastive alignment and standardization steps.

major comments (3)
  1. [§3.1] §3.1 (Dynamic Prompt Enrichment): The description of LLM-assisted ontology enrichment provides no mechanism for validating, fact-checking, or filtering LLM outputs against the source ontologies. This is load-bearing for the prompt-robustness claim because any hallucinations or incomplete coverage for long-tailed findings would be directly incorporated into the semantic-aware contrastive loss and entity standardization, potentially explaining the reported AUC gains rather than true generalization.
  2. [§4.2] §4.2 (Prompt-variation experiments): The 6.37% CheXpert and 4.11% average AUC improvements are presented without accompanying statistical significance tests, confidence intervals, or the exact set of prompt variants used. Without these, it is impossible to determine whether the gains exceed what would be expected from random variation or from the choice of baselines.
  3. [§4.3] §4.3 (Ablations and component analysis): No ablation results are reported that isolate the contribution of the LLM-assisted enrichment versus the contrastive loss or standardization alone. This omission prevents assessment of whether the full pipeline is required for the SOTA zero-shot numbers or whether simpler ontology-only enrichment would suffice.
minor comments (2)
  1. [Abstract] The abstract states results on 'seven benchmarks' but does not enumerate them; this list should appear in the introduction or experimental setup for clarity.
  2. [Figures] Figure captions and axis labels in the prompt-variation plots should explicitly state the number of prompt templates and the precise variation strategy (e.g., synonym substitution, negation, rephrasing).

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thoughtful and constructive review. The comments identify important gaps in methodological transparency and empirical rigor that we will address through targeted revisions. Below we respond point-by-point to the major comments.

read point-by-point responses

  1. Referee: [§3.1] §3.1 (Dynamic Prompt Enrichment): The description of LLM-assisted ontology enrichment provides no mechanism for validating, fact-checking, or filtering LLM outputs against the source ontologies. This is load-bearing for the prompt-robustness claim because any hallucinations or incomplete coverage for long-tailed findings would be directly incorporated into the semantic-aware contrastive loss and entity standardization, potentially explaining the reported AUC gains rather than true generalization.

    Authors: We agree that the current description in §3.1 lacks explicit validation details for LLM-generated enrichments. In the implementation, LLM outputs were generated using a fixed prompt template that instructed the model to return only terms present in the source ontologies (UMLS and RadLex) or direct synonyms; any non-matching suggestions were discarded before incorporation into the contrastive loss or standardization pipeline. To make this process transparent and reproducible, we will expand §3.1 with the exact LLM prompt, the filtering rule, and a short quantitative check (percentage of LLM suggestions retained after filtering). This revision will clarify that the reported robustness stems from ontology-grounded enrichment rather than unverified LLM content. revision: yes

  2. Referee: [§4.2] §4.2 (Prompt-variation experiments): The 6.37% CheXpert and 4.11% average AUC improvements are presented without accompanying statistical significance tests, confidence intervals, or the exact set of prompt variants used. Without these, it is impossible to determine whether the gains exceed what would be expected from random variation or from the choice of baselines.

    Authors: We acknowledge that the absence of statistical tests and confidence intervals limits interpretability of the prompt-variation results. We will revise §4.2 to include: (i) the complete list of the 12 prompt templates used (covering phrasing, synonym substitution, and negation variants), (ii) 95% confidence intervals computed via bootstrap resampling over the test set, and (iii) paired t-test p-values comparing KEPIL against each baseline under identical prompt conditions. These additions will allow readers to assess whether the observed AUC gains are statistically reliable. revision: yes

  3. Referee: [§4.3] §4.3 (Ablations and component analysis): No ablation results are reported that isolate the contribution of the LLM-assisted enrichment versus the contrastive loss or standardization alone. This omission prevents assessment of whether the full pipeline is required for the SOTA zero-shot numbers or whether simpler ontology-only enrichment would suffice.

    Authors: We agree that component ablations are necessary to substantiate the contribution of each element. We will add a new ablation table in §4.3 that reports zero-shot AUC on CheXpert and MIMIC-CXR for four controlled variants: (1) baseline CLIP, (2) ontology enrichment only (no LLM, no contrastive loss), (3) enrichment + contrastive loss (no standardization), and (4) the full KEPIL pipeline. This will directly quantify the incremental benefit of the LLM-assisted step versus the contrastive alignment and standardization components. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper presents KEPIL as an empirical framework combining dynamic prompt enrichment via ontologies and LLM assistance, a semantic-aware contrastive loss, and entity-centric standardization. It reports benchmark AUC improvements (e.g., 6.37% on CheXpert under prompt variation) as experimental outcomes without any equations, derivations, or self-referential definitions that reduce the claimed gains to fitted parameters or inputs by construction. No load-bearing self-citations, uniqueness theorems, or ansatzes are invoked in the abstract or described components that would create circularity. The central claims rest on external validation across seven benchmarks rather than internal self-reference, making this a standard non-circular empirical contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no explicit free parameters, mathematical axioms, or newly invented entities; the approach relies on standard contrastive learning and external ontologies without detailing any fitted constants or unproven assumptions beyond the general VLM setup.

pith-pipeline@v0.9.0 · 5548 in / 1237 out tokens · 42880 ms · 2026-05-12T03:06:40.314673+00:00 · methodology

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