Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

Duy-Anh Bui; Hai-Nam Duy Vuong; Kim-Ngan Thi Nguyen; Thien Van Luong; Tien-Cuong Nguyen; Trang Mai Xuan; Trong-Nghia Nguyen; Van-Dem Pham

REVIEW 4 major objections 4 minor 24 references

Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs

T0 review · 4 major / 4 minor · reviewed 2026-08-01 · deepseek-v4-flash

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Pith's one-line read Injecting a distilled ECG interpretation guide into every multimodal prompt yields more guideline-consistent, less hallucinated cardiac reports than a CNN+Grad-CAM+MLLM baseline, lifting BERTScore from 0.818 to 0.953.

desk verdict A sensible guide-injection idea for ECG report generation, but the headline numbers are internally inconsistent and the central claim is unverifiable as printed. read the letter →

arxiv 2607.20814 v1 pith:U5FR6PZH submitted 2026-07-23 cs.AI

Hai-Nam Duy Vuong , Duy-Anh Bui , Trong-Nghia Nguyen , Kim-Ngan Thi Nguyen , Trang Mai Xuan , Tien-Cuong Nguyen , Van-Dem Pham , Thien Van Luong This is my paper

classification cs.AI

keywords ElectrocardiogramExplainableAIGuidelineGroundingKnowledgeInjectionGrad-CAMMultimodalLargeLanguageModelsBERTScorePTB-XL

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

This paper proposes that the way to keep LLM-generated ECG reports honest is to anchor them in a distilled ECG Interpretation Guide rather than relying on the model's internal knowledge. The authors build a three-stage pipeline—CNN classification, Grad-CAM heatmaps, and guide-grounded report generation—and compare it with a strong CNN+Grad-CAM+MLLM baseline. On the PTB-XL test set, the guide-grounded version raises BERTScore, a semantic-similarity metric, from 0.818 to 0.953, and two blinded LLM judges prefer its reports 62–76% of the time. A sympathetic reader takes away that a fixed, non-retrieved guideline block is a practical lever against hallucination in structured clinical report generation.

What carries the argument

The ECG Interpretation Guide is the central object: a structured, sectioned teaching document distilled offline from textbook and guideline material via chunking, embedding, retrieval, noise compression, and final LLM synthesis. It is injected as a fixed global context block into every report-generation prompt, in contrast to per-case retrieval. The argument assigns an explicit evidence hierarchy—Grad-CAM heatmaps as primary visual evidence, the ECG image as context, the CNN fact pack as auxiliary, and the guide as a grounding reference for phrasing—so the guide constrains language and criteria use without driving the diagnosis itself.

What would settle it

Recompute the two BERTScore sub-scores and their arithmetic mean on the released outputs of the PTB-XL test set. If the mean does not reproduce 0.953 for the proposed method, or if permuting hypothesis-reference pairs does not produce a sharp drop, the headline claim fails. A complementary test: a blinded panel of cardiologists compares baseline and guide-grounded reports on the 200-sample subset; if clinicians show no preference, the perceived-consistency claim is not settled by the LLM judges alone.

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Extended reading notes

Core claim

The paper's central claim is that grounding multimodal LLM report generation in a distilled ECG Interpretation Guide, synthesized offline from authoritative ECG textbooks and guideline materials and injected as a fixed knowledge block for every sample, moves generated ECG impressions closer to reference reports while preserving classification performance. On the full PTB-XL test set, the proposed method raises BERTScore from 0.818 to 0.953 relative to the baseline, and two blinded LLM judges prefer guide-grounded reports in 62–76% of pairwise comparisons. The paper interprets these gains as evidence that fixed, non-retrieved guideline context reduces hallucination and encourages guideline-co

Load-bearing premise

The load-bearing premise is that a semantic-similarity score computed between German reference strings and English generated impressions—averaged with a machine-translated reference variant—is a valid proxy for closer alignment with reference reports and for reduced hallucination; the abstract's headline number (0.953) matches only the translated-reference sub-score, not the paper's stated arithmetic mean of the two settings.

Editorial extensions

If this is right

Guide grounding raises the semantic quality of generated impressions over the baseline on the full test set while preserving classification performance, so the method does not trade predictive accuracy for textual plausibility.
The fixed, non-retrieved guide means the entire domain knowledge fits within one context block, keeping inference simple, reproducible, and free of per-case retrieval overhead.
The modular pipeline allows the CNN, Grad-CAM explainer, guide, and multimodal LLM to be upgraded independently; the guide can be refreshed as textbooks and guidelines change.
LLM-judge preferences in the 62–76% range, across two different judges, indicate perceived consistency gains that the paper reads as supporting increased clinician trustworthiness.
Because the guide is injected identically for every ECG, the method is deterministic in its knowledge grounding and does not depend on a retrieval step whose results might vary across samples.

Reading between the lines

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

A reader should not assume the BERTScore gap transfers directly to human clinical judgment: the paper's only human signal is a small expert review, while the main judging is LLM-based. A blinded cardiologist comparison on the same 200-sample subset would settle whether clinicians actually prefer guide-grounded reports.
Because the guide is a fixed English-language block, part of the BERTScore gain may reflect generated impressions adopting guide-like phrasing rather than clinically richer content; isolating lexical-style effects from content fidelity would require evaluation against reference diagnostic content, not just reference wording.
The approach generalizes as a recipe for structured clinical reporting: offline distillation of stable domain knowledge into a compact context block can replace per-case retrieval. Testing on other imaging modalities, such as chest X-ray or echocardiography reports, would probe that generality.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The paper's central number doesn't survive contact with its own protocol. The abstract reports an average BERTScore improvement from 0.818 to 0.953, but the paper states the main result is the arithmetic mean of two settings: cross-lingual and translated-reference. For the method, those F1 scores are 0.982 and 0.953, whose mean is 0.9675, not 0.953. The abstract's headline is just the translated-reference sub-score. Worse, the baseline BERTScore is identical to three decimals (0.816/0.821/0.818) in both Tables 3 and 5, across two different evaluation settings. With thousands of samples, that is essentially impossible for real data; a duplicated table or an unreported protocol change are the plausible explanations. These aren't cosmetic issues — the entire empirical contribution rests on this comparison, and as printed the effect size is unverifiable.

Credit where it's due: the idea of injecting a fixed, distilled ECG interpretation guide into a multimodal prompt is a reasonable extension of known prompting techniques, and the paper applies it to ECG report generation with a clean ablation against a CNN+Grad-CAM+MLLM baseline. The pipeline is modular, the evaluation uses a public dataset, and they say they release the guide itself — if the repository link works, that's a useful artifact. The qualitative analysis and the LLM-judge preference results are suggestive, though they measure perceived style more than clinical truth.

The soft spots go beyond the arithmetic. Cross-lingual BERTScore between German references and English generations is a weak proxy for alignment, and the translated-reference variant at least makes the comparison fairer but still indirect. The discard rate for invalid JSON outputs is never reported, so the two conditions being compared may rest on different subsets of samples. The conclusion that guide grounding "reduces hallucinations" is not supported by direct evidence; no hallucination metric is used. The paper's own limitations section acknowledges the guide will become outdated and that LLM judging is approximate, which is honest but doesn't fix the main numbers.

Who is this for? Researchers working on ECG report generation or knowledge-grounded medical LLMs might find the guide-injection pipeline worth trying, and the released guide could be reusable. But the empirical claim needs correction before the results can be trusted. With corrected tables, reported variance, discard counts, and evaluation code, this could be a decent workshop or applied-domain paper. As written, I would not cite it for the quantitative result.

For peer review: send it to referees. The flaws are concrete and fixable, and the topic deserves scrutiny. A good referee will catch the inconsistencies; the authors then either produce clean numbers or the paper should be rejected.

Referee Report

4 major / 4 minor

Summary. The paper proposes a guide-grounded multimodal LLM pipeline for ECG report generation. A CNN and Grad-CAM produce class probabilities and heatmaps from 12-lead ECG images; a distilled 'ECG Interpretation Guide' is injected as a fixed knowledge block into a multimodal LLM prompt alongside the image, overlay, and CNN fact pack. The central claim is that guide grounding raises the average BERTScore of generated impressions from 0.818 to 0.953 relative to a CNN+Grad-CAM+MLLM baseline, and that LLM judges prefer the guide-grounded reports. Experiments are on PTB-XL, with BERTScore computed under two reference-language settings and a forced-choice LLM judging protocol on a 200-sample subset.

Significance. If the reported gains were reproducible, the work would be a useful incremental contribution to explainable ECG reporting: it combines a standard CNN/Grad-CAM pipeline with a fixed knowledge-injection strategy that is simpler than per-case RAG, and it ships a public guide and code repository. The paper also includes some good evaluation practices: a permutation sanity check for BERTScore, repeated blinded LLM judging with randomized order, and explicit separation of two reference settings. However, the central quantitative claim is currently undermined by internal numerical inconsistencies in the reported BERTScore tables, and the effect size cannot be verified without corrected numbers and raw evaluation outputs.

major comments (4)

[Abstract; §3.1; Tables 3 and 5] The main reported BERTScore is defined in §3.1 as the arithmetic mean of the cross-lingual and translated-reference settings. With Ours F1 = 0.982 (Table 3) and Ours F1 = 0.953 (Table 5), the mean is 0.9675, yet the Abstract and Conclusions report 0.953. That number is only the translated-reference sub-score, not the stated average. This is not a minor wording issue: the paper's headline quantitative result does not follow from its own definition.
[Tables 3 and 5] The baseline rows are identical to three decimal places in both settings (Precision 0.816, Recall 0.821, F1 0.818), even though one setting scores German references against English generations and the other scores English-translated references. With thousands of test samples, exact equality to three decimals across two distinct reference transformations is implausible. This suggests either a duplicated table or an unreported protocol difference. Since the baseline is the comparison point for the claimed improvement, the effect size is unverifiable as presented.
[§3.2] The BERTScore is averaged only over samples with valid generated outputs ('valid JSON and a non-empty impression field'), but the number of discarded outputs is not reported per method or per condition. If differing discard rates apply to baseline versus guide-grounded generation, the two BERTScore values may be computed on different sample subsets, biasing the comparison. The authors should report the discard counts and, ideally, provide the generation outputs or a per-sample score distribution.
[§3.2, Table 4] The LLM-judge results are reported as mean win-rates over 5 runs, but the standard deviation is never given even though §3.1 says it will be reported 'when applicable'. With 192 samples and 5 runs, variance information is needed to assess whether the 62% vs 38% and 76% vs 24% preferences are stable. This is secondary to the BERTScore issues, but it should be added in a revision.

minor comments (4)

[General] Several table captions contain typos, e.g., 'T able 1', 'T able 2', 'T able 3', 'T able 4', 'T able 5'. These should be corrected.
[§2.2 vs §3.1] Stage 2 repeatedly states that the compression step 'forbids summarization' and preserves all medical content, but §3.1 says the guide was constructed 'by summarizing knowledge in medical books'. This is an internal inconsistency in describing the guide-creation procedure.
[§2.2] The description of chunk sizes is confusing: the guide is said to be built from chunks 'up to 700,000 characters per chunk', yet the final guide is described later as a fixed block fitting into the context window. Clarify the sizes of the intermediate chunks and of the final guide.
[References] The guide is described as distilled from textbooks and 'guideline materials', but the reference list includes only two textbooks and no explicit clinical guideline documents. Provide the provenance or citations for the guidelines.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the central claim is an empirical ablation on an external benchmark; the abstract's BERTScore arithmetic inconsistency is a reporting error, not a construction.

full rationale

The claimed result is an empirical ablation, not a derivation. Stage 1 trains a ResNet-50 CNN on PTB-XL labels; Stage 2 distills an ECG Interpretation Guide offline from external textbooks [4,15]; Stage 3 generates reports from a frozen API MLLM conditioned on the image, Grad-CAM overlay, CNN fact pack, and (for 'Ours') the fixed guide. BERTScore is then computed against the PTB-XL report field, an external reference. No parameter of the evaluation is fitted to the reported BERTScore, no quantity in the claim is defined in terms of the other claimed quantity, and the baseline [21] and LLM-judge protocol [24] are external prior works rather than self-citations. The paper's own limitations (guide staleness; LLM judge is 'an approximation to human expert assessment') concern validity, not circularity. I flag one non-circular reporting inconsistency: §3.1 states 'Our main reported BERTScore is the arithmetic mean of the two settings above', but the Abstract's '0.953' equals only the translated-reference sub-score in Table 5, not the mean (0.982+0.953)/2=0.9675, and the baseline P/R/F1 in Tables 3 and 5 are identical to three decimals. This undermines the reproducibility of the headline effect but does not make the claim equivalent to its inputs by construction.

Assumptions & free parameters 0 free parameters · 4 assumptions · 0 invented entities

No fitted parameters or invented entities. The pipeline is an ablation of prompt content; all stated hyperparameters (temperature, top_k, alpha) are shared across baseline and method. The load-bearing assumptions are the accuracy of the distilled guide and the validity of the evaluation metrics.

assumptions (4)

domain assumption The ECG Interpretation Guide synthesized from the two cited textbooks adequately represents authoritative ECG diagnostic criteria.
The claim that outputs are 'guideline-consistent' rests on the guide's accuracy (Stage 2, §2.2).
domain assumption Cross-lingual BERTScore with xlm-roberta-large and Google Translate-translated references measures semantic quality of diagnostic reports.
The central metric in Tables 3 and 5 is assumed to reflect report quality (Stage 3.1).
domain assumption The PTB-XL 'report' field is an appropriate reference for English generated impressions.
References are German strings; the paper assumes they can serve as ground truth for English impressions.
domain assumption The baseline [21] was reproduced with equivalent fidelity.
Comparison validity depends on the baseline implementation matching the cited work (implementation details).

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Cite this review

Pith. "Pith review of Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs." pith.science (2026). https://pith.science/paper/U5FR6PZH

@misc{pith2026260720814,
  author       = {Pith},
  title        = {Pith review of: Enhancing Explainable Cardiac Diagnosis with Guide-Grounded Multimodal LLMs},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/U5FR6PZH}},
  note         = {Machine review of arXiv:2607.20814}
}

read the original abstract

The electrocardiogram (ECG) is a cornerstone of cardiac as- sessment, yet clinical deployment of deep learning models remains con- strained by limited interpretability and the hallucination risk of large language models (LLMs). Existing CNN+Grad-CAM+multimodal LLM frameworks can generate ECG reports, but their explanations are often only weakly grounded in established diagnostic criteria, reducing trust- worthiness and reproducibility. We propose a guide-grounded multimodal framework that explicitly anchors report generation in curated clinical knowledge. A convolutional neural network (CNN) and Grad-CAM first produce class probabilities and class-specific heatmaps from 12-lead ECG images. In parallel, authoritative ECG textbooks and guideline materials are distilled offline into a structured ECG Interpretation Guide, which is injected as a fixed knowledge block for every sample. Conditioned on the ECG image, Grad-CAM overlay, CNN-derived fact pack, and the in- jected guide, a multimodal LLM generates structured diagnostic reports with guideline-consistent terminology and criteria usage. Experiments on the full PTB-XL test set demonstrate that guide grounding improves se- mantic quality and perceived consistency of generated reports while pre- serving competitive classification performance. In particular, our method increases the average BERTScore of generated impressions from 0.818 to 0.953 relative to a strong CNN+Grad-CAM+MLLM baseline, indicat- ing closer alignment with reference reports. These findings suggest that injecting a distilled interpretation guide into the multimodal prompting pipeline offers a practical pathway to reduce hallucinations and enhance the clinical plausibility of LLM-based ECG explanations, bringing ex- plainable cardiac diagnosis closer to real-world deployment.

Figures

Figures reproduced from arXiv: 2607.20814 by the authors.

**Figure 1.** Guide-grounded multimodal report generation. CNN classifier. Let x denote the rendered ECG image. A CNN classifier fθ outputs diagnostic probabilities p = fθ(x) ∈ [0, 1]K, (1) where K=5 corresponds to the PTB-XL diagnostic superclasses. Since each ECG may contain multiple superclasses, we use a multi-label setup with sigmoid outputs. We employ a ResNet-50 backbone with a task-specific classification head. Grad-CAM … view at source ↗

**Figure 2.** Prompt structure for ECG guide. corresponding retrieved chunks (pages/paragraphs) to be included as supporting material for the guide. Compression. The retrieved chunks are then compressed to remove duplicated lines, repeated headers/footers, and obvious boilerplate. The instruction explicitly forbids summarization and forbids removing medically relevant content; the goal is only to reduce noise while preserving c… view at source ↗

**Figure 3.** BERTScore comparison between the baseline and our guide-grounded method. Report Quality (BERTScore) We evaluate reports using BERTScore [22] against the reference diagnostic statement strings (the report field in PTB-XL). BERTScore is computed on the official PTB-XL test split and averaged over all samples with valid generated outputs (valid JSON and a non-empty impression field). Because references are in German wh… view at source ↗

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Reference graph

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