CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models

Abdulrahman Mohamed Selim; Daniel Sonntag; Devansh Srivastav; Hasan Md Tusfiqur Alam; Md Abdul Kadir; Md Moktadirul Hoque Shuvo

REVIEW 4 major objections 4 minor 18 references

CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models

T0 review · 4 major / 4 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read AI radiology reports become traceable to editable clinical concepts.

desk verdict A clean demo companion with a live UI, but the abstract's claims about mitigating hallucinations are unsupported and the cross-modal embedding assumption is unvalidated. read the letter →

arxiv 2504.20898 v2 pith:WYGDB2TN submitted 2025-04-29 cs.AI cs.CVcs.IR

Hasan Md Tusfiqur Alam , Devansh Srivastav , Abdulrahman Mohamed Selim , Md Abdul Kadir , Md Moktadirul Hoque Shuvo , Daniel Sonntag This is my paper · ORCID

classification cs.AIcs.CVcs.IR

keywords InterpretableradiologyreportgenerationDiseaseclassificationMedicalimagingConceptBottleneckModelsRetrieval-AugmentedInformationRetrievalVision-languageLargelanguage

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 argues that radiology report generation becomes both more interpretable and more reliable when the image is first distilled into human-understandable clinical concepts, and only then handed to a retrieval-augmented report writer. The authors build a pipeline in which a vision-language model scores concepts such as 'pulmonary consolidation' for a chest X-ray, a classifier reads only those scores to predict the disease, and five specialized agents retrieve clinical knowledge and write a tailored report. The point of the design is to give clinicians something they can inspect and edit before the text is produced: each concept has a contribution score and a saliency heatmap, and changing a score changes the downstream prediction. If the approach works, it addresses the two obstacles that keep generative models out of the reading room, namely opaque reasoning and hallucinated findings. The paper is a demonstration and notes that formal usability studies in real clinical settings are still future work.

What carries the argument

The central object is the concept bottleneck: a layer that forces the final prediction to depend only on a vector of human-understandable clinical concepts rather than on raw image features. In this implementation the concept set is generated by an LLM, each concept is embedded with a text encoder, the image is embedded with a chest-X-ray vision-language model, and cosine similarity between the two embedding sets forms a matrix; max pooling condenses the matrix into a concept vector, a fully connected layer classifies from it, and the weight-vector cross-product produces contribution scores while the similarity matrix doubles as the source of saliency heatmaps. This single bottleneck carries the whole interpretability argument, and the editable concept list is the mechanism by which a clinician can intervene in the prediction.

What would settle it

Take a set of chest X-rays with expert-annotated findings and compute the concept scores and heatmaps: if the heatmap for 'pulmonary consolidation' does not localize to the annotated consolidation, or if the cosine-similarity ranking between the image and concept texts changes substantially when the two embeddings come from a jointly trained multimodal model, the grounding claim collapses. A second check is intervention fidelity: if editing a concept score leaves the predicted disease unchanged for most images, the editable-interpretability claim fails.

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Extended reading notes

Core claim

On its own terms, the central discovery is a workflow: placing a concept bottleneck in front of a multi-agent retrieval-augmented generator, with an interactive editing loop joining the two. The concept bottleneck maps each image to a normalized vector of clinically meaningful concept scores, classifies the disease from that vector alone, and derives both per-concept contribution scores and saliency heatmaps from the same similarity matrix. The multi-agent stage then takes those concepts and retrieved clinical documents to produce a final report, so the report text is anchored to externally sourced evidence rather than generated from the image only. The authors claim this yields transparent disease classifications, tailored evidence-based reports, and reduced hallucination and opacity compared with end-to-end image-to-report generation.

Load-bearing premise

The load-bearing premise is that cosine similarity between image embeddings from one model and text embeddings from a different, separately trained model measures genuine clinical relevance, so that the resulting concept scores, heatmaps, and classifications are meaningful rather than artifacts of mismatched embedding spaces.

Editorial extensions

If this is right

A radiologist can audit a predicted disease by reading the concepts that drove it and checking the heatmaps that localize those concepts in the image.
Retrieval-grounded report generation should reduce invented findings, because the writer synthesizes from retrieved clinical documents rather than free generation.
Editing a concept score provides a concrete intervention: the user can correct a mistaken or missing concept and the classification and report should respond.
The conversational chat interface turns the system into a context-aware assistant that can answer follow-up questions about the image, the report, or the clinical condition.

Reading between the lines

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

The most important unstated risk is that the concept scores are computed by cosine similarity between image embeddings from one model and text embeddings from a different model; those embedding spaces were never aligned, so the similarity ranking may not mean what it appears to mean. A cross-encoder or jointly trained multimodal model would provide a direct comparison.
The concept set itself is assumed to be medically complete because an LLM generated it; a missing concept cannot enter the bottleneck, so the system could silently ignore a finding a radiologist would consider essential. An expert coverage audit would test this.
The hallucination-mitigation claim is asserted rather than measured. A controlled benchmark comparing this pipeline with a plain image-to-report baseline on factual-consistency metrics would turn the claim into a number.
The authors state that formal usability studies in real clinical settings remain future work, so the trust and workflow benefits are, for now, demonstrated in an interactive prototype rather than established in practice.

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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

Quick take: this is a three-page demo companion, not a scientific paper. The architecture is from the same authors' ECIR 2025 paper; what's new here is the chat interface and the editable concept scores. The abstract claims the system 'delivers interpretable predictions, mitigates hallucinations, and generates high-quality, tailored reports.' That is more than the demonstration supports.

What's good: the code and demo are public and the UI is genuinely interactive. You can upload a CXR, see concepts, toggle heatmaps, edit concept scores, and watch the predicted class change in response. For a demo, that is a solid artifact. The multi-agent RAG with five specialized agents is also concretely implemented, and the design choice to let clinicians adjust concept scores is a useful feature for exploring trust.

Now the soft spots. The biggest problem is the complete absence of evaluation. There is no classification accuracy, no report quality metric, no hallucination measurement, and no clinician study. The conclusion itself says 'formal usability studies in real clinical settings are yet to be conducted.' So the abstract's 'mitigates hallucinations' is not backed by any evidence. That is not a minor omission; it is the main claim.

The second soft spot is exactly what the stress-test flags: the cross-modal cosine similarity between CheXagent image embeddings and Mistral text embeddings. These are independent models with no shared embedding space, no projection, and no alignment step. Cosine similarity is only semantic within one space. Without some calibration or qualitative check against known labels, the concept scores, contribution scores, and heatmaps are not established as meaningful. This is load-bearing because every interpretable output the paper advertises derives from that similarity matrix. I checked the paper for a counterexample or a sanity check—there is none.

Novelty is thin relative to the authors' own ECIR paper, but for a companion demo track that is acceptable. The paper is honest about the lack of user studies, so it is not deceptive—it is just over-claimed in the abstract and underevaluated.

Who is this for? Someone building demo systems for clinical AI might find the UI and the agent scaffolding a useful starting point. Someone looking for evidence that CBM plus RAG reduces hallucinations or improves interpretability will not find it here.

Recommendation: I would not send this to a full peer-review venue as a research contribution. The evidence gap is too wide. For a demo track, it is fine—the artifact is inspectable and the interactive editing idea is worth showing. But the claims in the abstract need to be scaled way back unless the authors add even basic quantitative results.

Referee Report

4 major / 4 minor

Summary. The paper presents CBM-RAG, a framework that couples a Concept Bottleneck Model with a multi-agent Retrieval-Augmented Generation pipeline for chest X-ray report generation. The CBM component computes cosine similarity between CheXagent image embeddings and Mistral text embeddings for an LLM-generated set of clinical concepts, max-pools the resulting matrix into a normalized concept vector, and feeds it to a fully connected classifier trained on the COVID-QU dataset. Contribution scores and saliency heatmaps are derived from the classifier weights and the similarity matrix. The multi-agent RAG component uses five agents (three disease-specific ReAct agents, a Radiologist Agent, and a Report Writer Agent) with NIH database retrieval to produce final reports. The authors describe an interactive UI with editable concept scores, heatmap toggles, and a chat interface. The abstract and conclusion claim interpretable predictions, mitigation of hallucinations, and generation of high-quality tailored reports; the conclusion admits that formal usability studies have not yet been conducted.

Significance. If the claims were substantiated, integrating concept-bottleneck interpretability with multi-agent RAG would be a useful direction for clinical decision support. The paper explicitly ships a public code repository and an online demo, which is a strength for reproducibility and community uptake. However, the contribution as written is a system demonstration: no classification accuracy, no report quality metric, no hallucination measurement, no concept-validity check, and no clinician or user study appear anywhere in the manuscript. The interpretability mechanism also rests on an unvalidated cross-modal embedding similarity assumption. The significance of the work therefore currently lies in the proposed architecture and interactive interface rather than in established empirical evidence.

major comments (4)

[Section 2, similarity matrix computation] The similarity matrix is computed as cosine similarity between image embeddings from CheXagent and text embeddings from the Mistral embed model. These are independent models trained separately with no described shared embedding space, projection, or alignment step. Because the concept vector, the normalized input to the fully connected classifier, the contribution scores, and the saliency heatmaps all derive from this matrix, the interpretability claim rests entirely on the unvalidated assumption that cross-model cosine similarity is semantically meaningful. The paper should either replace this mechanism with a jointly trained multimodal encoder (e.g., BiomedCLIP, which is already cited) or provide a calibration experiment showing that similarity scores correlate with ground-truth concept presence on a held-out set. Without such evidence, the concept scores and heatmaps may not measure the intended clinical concepts.
[Abstract and Conclusion, evaluation claims] The abstract states that the framework 'deliver[s] interpretable predictions, mitigate[s] hallucinations, and generate[s] high-quality, tailored reports,' and the conclusion repeats that the framework 'mitigat[es] hallucination and opacity issues.' However, the manuscript reports no classification accuracy, no report quality metric (e.g., BLEU, ROUGE, or a clinical correctness metric), no hallucination measurement, and no radiologist or user study. The Conclusion itself concedes that 'formal usability studies in real clinical settings are yet to be conducted.' These claims therefore exceed the evidence presented. The authors should either add a quantitative evaluation of report quality and hallucination rate for the actual system or substantially temper the abstract and conclusion to describe intended capabilities rather than demonstrated ones.
[Section 2, disease classification component] The fully connected layer trained on COVID-QU is described but no training details, hyperparameters, or evaluation results are provided. The paper never reports classification accuracy, precision, recall, F1, or a confusion matrix, and it does not compare against a standard CXR classifier or an uninterpretable baseline. Without this information, the statement that the system provides 'transparent disease classification' is unsupported. At minimum, the authors should report classification performance on the COVID-QU test split and examine whether the concept bottleneck substantially degrades accuracy relative to a non-interpretable classifier, since that trade-off is central to the CBM promise.
[Section 2, LLM-generated concept set] The concept set is generated automatically by an LLM, but the paper gives no details about the concept vocabulary: the number of concepts, the prompt used, whether concepts were deduplicated or filtered, or whether the set was validated against a medical ontology or clinician feedback. If the concept set is incomplete, contains spurious terms, or uses inconsistent phrasing, the contribution scores and heatmaps will be misleading even if the embedding alignment issue is resolved. The authors should describe how the concept set was generated, show the actual concepts used, and provide some evidence of medical completeness or correctness.

minor comments (4)

[References [5] and Section 2] The paper refers to the 'Mistral embed model' but cites Mistral 7B, which is a language model and not specifically an embedding model. Clarify the exact embedding model name and version used for the text embeddings to support reproducibility.
[Section 3, user interface] The interface description would benefit from explicit mention of whether the editable concept scores are constrained to the [0,1] range and how the system reacts when a user edits a concept score that conflicts with the original similarity-based value.
[Section 2, RAG system] The multi-agent RAG description is brief; providing the prompts used for the ReAct agents and the retrieval top-k or reranking strategy would help readers understand how the report content is grounded and how hallucination is supposedly reduced.
[References] Reference [3] is the COVID-QU dataset paper and is appropriate, but the paper should also state which train/test split was used and whether the class distribution was balanced, since this affects any future comparisons.

Circularity Check

0 steps flagged · score 1.0 of 10

No material circularity: the claimed disease prediction is a genuine output of a trained linear classifier over embedding-derived concept vectors, and no prediction reduces to a fitted input or self-citation by construction.

full rationale

The derivation chain is self-contained with respect to the advertised claims. In Section 2, concept scores are computed as cosine similarities between CheXagent image embeddings and Mistral text embeddings, max-pooled into a vector, normalized, and fed into a fully connected classifier trained on COVID-QU labels; the disease prediction is therefore a genuine output of a trained model, not a renamed fit. Contribution scores are the standard linear attribution of that classifier (weight matrix times concept vector), and saliency heatmaps are derived directly from the similarity matrix, so no interpretability artifact is a fitted parameter disguised as a prediction. The only self-citation, Ref. [1] by the same DFKI authors, appears in Section 2's “Building on prior works [1, 11, 16], we use LLMs to automatically acquire a set of concepts for classification”; it is a methodological pointer alongside two external works [11, 16], not an imported uniqueness theorem, and it does not force any result. Section 4's admission that “formal usability studies in real clinical settings are yet to be conducted” is an evaluation gap, not circularity. The cross-modal cosine-similarity assumption between independently trained encoders is a validity risk for the interpretability claim, but it is not an output being equivalent to an input by construction, so it does not raise the circularity score.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The system is assembled from existing components (CheXagent, Mistral, concept bottleneck models, and ReAct agents). Its central interpretability claim rests on assumptions about cross-model embedding alignment, the medical validity of LLM-generated concepts, and unvalidated saliency localization, none of which are tested. The classifier adds a trained linear layer with unreported parameters. No invented scientific entities are introduced.

free parameters (3)

Fully connected layer weights = not reported
The classifier that maps the normalized concept vector to the three disease classes is trained on COVID-QU; no weights, loss, or hyperparameters are given, yet these weights determine the contribution scores used for interpretability.
LLM-generated concept vocabulary = not reported
The concept set is acquired automatically from an LLM; its size, selection prompt, and clinical coverage are unspecified, and it directly defines the bottleneck representation.
Concept vector normalization = 0 to 1
The concept vector is normalized to the 0 to 1 range for interpretability; the choice of normalization affects the inputs to the fully connected layer and the resulting contribution scores.

assumptions (5)

domain assumption CheXagent image embeddings and Mistral text embeddings live in a compatible vector space such that cosine similarity is semantically meaningful.
Section 2 concatenates embeddings from two independently trained models without any alignment or projection; the entire concept scoring depends on this.
domain assumption LLM-generated clinical concepts accurately and comprehensively represent the findings relevant to COVID-19, pneumonia, and normal chest X-rays.
The concept set is generated automatically using LLMs, citing prior works; no medical validation or expert review of the concept set is reported.
ad hoc to paper Saliency regions derived from the similarity matrix localize the concept in the image.
The paper states saliency regions for each concept are derived from the similarity matrix without proof that cosine similarity peaks correspond to anatomical locations.
domain assumption The COVID-QU dataset labels are accurate for the three classes.
Classification training relies on COVID-QU; no label audit or error analysis is provided.
domain assumption Retrieved NIH documents are relevant and up to date, grounding the generated report.
The multi-agent RAG queries a pre-configured NIH database; no verification of retrieval quality or recency is described.

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Cite this review

Pith. "Pith review of CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models." pith.science (2026). https://pith.science/paper/WYGDB2TN

@misc{pith2026250420898,
  author       = {Pith},
  title        = {Pith review of: CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WYGDB2TN}},
  note         = {Machine review of arXiv:2504.20898}
}

read the original abstract

Advancements in generative Artificial Intelligence (AI) hold great promise for automating radiology workflows, yet challenges in interpretability and reliability hinder clinical adoption. This paper presents an automated radiology report generation framework that combines Concept Bottleneck Models (CBMs) with a Multi-Agent Retrieval-Augmented Generation (RAG) system to bridge AI performance with clinical explainability. CBMs map chest X-ray features to human-understandable clinical concepts, enabling transparent disease classification. Meanwhile, the RAG system integrates multi-agent collaboration and external knowledge to produce contextually rich, evidence-based reports. Our demonstration showcases the system's ability to deliver interpretable predictions, mitigate hallucinations, and generate high-quality, tailored reports with an interactive interface addressing accuracy, trust, and usability challenges. This framework provides a pathway to improving diagnostic consistency and empowering radiologists with actionable insights.

Figures

Figures reproduced from arXiv: 2504.20898 by the authors.

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Reference graph

Works this paper leans on

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Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.

[1] [1]

Hasan Md Tusfiqur Alam, Devansh Srivastav, Md Abdul Kadir, and Daniel Son- ntag. 2025. Towards Interpretable Radiology Report Generation via Concept Bottlenecks Using a Multi-agentic RAG. In Advances in Information Retrieval - 47th European Conference on Information Retrieval, ECIR 2025, Lucca, Italy, April 6-10, 2025, Proceedings, Part III (Lecture Notes...

2025

[2] [2]

Zhihong Chen, Maya Varma, Jean-Benoit Delbrouck, Magdalini Paschali, Louis Blankemeier, Dave Van Veen, Jeya Maria Jose Valanarasu, Alaa Youssef, Joseph Paul Cohen, Eduardo Pontes Reis, Emily Tsai, Andrew Johnston, Cameron Olsen, Tanishq Mathew Abraham, Sergios Gatidis, Akshay S Chaudhari, and Curtis Langlotz. 2024. CheXagent: Towards a Foundation Model fo...

work page 2024

[3] [3]

Muhammad EH Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi, et al. 2020. Can AI help in screening viral and COVID-19 pneumonia? Ieee Access 8 (2020), 132665–132676

work page 2020

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Christopher Ifeanyi Eke and Liyana Shuib. 2024. The role of explainability and transparency in fostering trust in AI healthcare systems: a systematic literature review, open issues and potential solutions. Neural Computing and Applications (2024), 1–36

work page 2024

[5] [5]

Albert Q Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, De- vendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, et al . 2023. Mistral 7B. arXiv preprint arXiv:2310.06825 (2023)

arXiv 2023

[6] [6]

Ujjwal Singh Kathait, Anamika Rana, Rahul Chauhan, and Ruchira Rawat. 2024. A Comprehensive Review of Interpretability in AI and Its Implications for Trust in Critical Applications. In 2024 4th International Conference on Sustainable Expert Systems (ICSES). IEEE, 1683–1693

work page 2024

[7] [7]

Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pier- son, Been Kim, and Percy Liang. 2020. Concept bottleneck models. InInternational conference on machine learning . PMLR, 5338–5348

work page 2020

[8] [8]

Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020. Retrieval-augmented generation for knowledge-intensive nlp tasks. Advances in Neural Information Processing Systems 33 (2020), 9459–9474

2020