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arxiv: 2605.01779 · v1 · submitted 2026-05-03 · 💻 cs.CV

MedScribe: Clinically Grounded CT Reporting through Agentic Workflows

Giuseppe A. Orlando , Paolo Papotti , Maria A. Zuluaga , Olivier Humbert , Marco Lorenzi This is my paper

Pith reviewed 2026-05-08 19:17 UTC · model grok-4.3

classification 💻 cs.CV
keywords radiology report generationCT imagingvision-language modelsagentic workflowsmedical image analysisfactual consistency3D medical imaging
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The pith

MedScribe improves CT radiology reports by using an LLM to iteratively invoke diagnostic tools for evidence gathering instead of one-shot encoding.

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

The paper presents MedScribe as a framework that turns automated CT report generation into an iterative process driven by hypotheses rather than a single compression of the full scan. A large language model decides which pathology-specific tools to call, extracts localized 3D features, and queries a retrieval space built from matching textual evidence. This sequence is intended to produce reports with fewer unsupported findings and clearer links between image data and text. The approach achieves higher clinical accuracy and consistency on two public CT datasets than existing vision-language models, all without additional fine-tuning of the core models.

Core claim

MedScribe reformulates report generation as an iterative evidence acquisition process rather than a single-pass encoding task. It models reporting as a sequential decision process in which a large language model dynamically invokes pathology-specific diagnostic tools to extract localized volumetric features. These structured features query a multidimensional retrieval space aligned with pathology-specific textual evidence. By explicitly accumulating quantitative evidence prior to synthesis, the framework enforces fine-grained grounding and reduces unsupported claims.

What carries the argument

The hypothesis-driven agentic workflow in which an LLM sequentially calls pathology-specific diagnostic tools to accumulate localized volumetric features and query a matching retrieval space before report synthesis.

Load-bearing premise

That the pathology-specific diagnostic tools reliably extract accurate localized volumetric features and that the multidimensional retrieval space is correctly aligned with pathology-specific textual evidence.

What would settle it

Testing MedScribe on a CT dataset containing pathologies that the diagnostic tools are known to miss or mislocalize, then checking whether generated reports lose their accuracy and consistency advantage over baseline VLMs.

Figures

Figures reproduced from arXiv: 2605.01779 by Giuseppe A. Orlando, Marco Lorenzi, Maria A. Zuluaga, Olivier Humbert, Paolo Papotti.

Figure 3
Figure 3. Figure 3: Fine-grained understanding of pleural effusion laterality in report generation. Correct and incorrect findings view at source ↗
Figure 4
Figure 4. Figure 4: System instruction template and user message wrapper used for snippet extraction, shared across all 18 view at source ↗
Figure 5
Figure 5. Figure 5: Four-shot examples for arterial wall calcification. Examples for the remaining pathologies follow the same view at source ↗
read the original abstract

Vision-language models (VLMs) have shown potential for automated radiology report generation, yet existing approaches rely on global embedding compression of volumetric data, often leading to hallucinated findings and limited anatomical grounding in 3D CT imaging. We introduce MedScribe, a hypothesis-driven framework that reformulates report generation as an iterative evidence acquisition process rather than a single-pass encoding task. MedScribe models reporting as a sequential decision process in which a large language model dynamically invokes pathology-specific diagnostic tools to extract localized volumetric features. These structured features are used to query a multidimensional retrieval space aligned with pathology-specific textual evidence. By explicitly accumulating quantitative evidence prior to synthesis, the framework enforces fine-grained grounding and reduces unsupported claims. Without task-specific fine-tuning, MedScribe improves clinical accuracy, factual consistency, and interpretability on CT-RATE and RadChestCT compared to state-of-the-art 2D and 3D VLMs, demonstrating the value of hypothesis-driven reasoning for reliable medical image reporting.

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

2 major / 1 minor

Summary. The paper introduces MedScribe, a hypothesis-driven agentic framework for CT radiology report generation. It models reporting as an iterative evidence-acquisition process in which an LLM dynamically invokes pathology-specific diagnostic tools to extract localized volumetric features from 3D scans; these features query a multidimensional retrieval space aligned with pathology-specific textual evidence. Evidence is accumulated before final synthesis. The central claim is that, without task-specific fine-tuning, MedScribe improves clinical accuracy, factual consistency, and interpretability over state-of-the-art 2D and 3D VLMs on the CT-RATE and RadChestCT datasets.

Significance. If the tool-based extraction and retrieval alignment are shown to be reliable, the work could meaningfully advance medical VLM design by replacing monolithic global-embedding approaches with explicit, modular evidence accumulation. This direction addresses documented hallucination and grounding problems in volumetric reporting and could influence agentic systems in other clinical imaging tasks. The absence of independent quantitative validation for the core tools, however, limits the ability to attribute any observed gains specifically to the framework.

major comments (2)
  1. Abstract: the claim that MedScribe 'improves clinical accuracy, factual consistency, and interpretability' on CT-RATE and RadChestCT is presented without any numerical metrics, confidence intervals, statistical tests, or even the names of the evaluation metrics used. Because the performance gains constitute the primary empirical result, this omission prevents assessment of whether the improvements are substantive or statistically supported.
  2. Experiments section (assumed §4): the central mechanism depends on the pathology-specific diagnostic tools reliably extracting accurate localized volumetric features and on correct alignment of the multidimensional retrieval space with textual evidence. No precision, recall, or radiologist-agreement figures are supplied for tool outputs on the evaluation sets, nor is any ablation or alignment metric reported for the retrieval component. If either component underperforms, the grounding benefit and the reported superiority over direct VLMs would not follow.
minor comments (1)
  1. The description of how the retrieval space is constructed and queried would be clearer with a small diagram or pseudocode snippet illustrating the dimensionality and matching procedure.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. The comments correctly identify areas where additional quantitative detail is needed to support the central claims. We address each point below and will incorporate the requested information in the revised manuscript.

read point-by-point responses

  1. Referee: [—] Abstract: the claim that MedScribe 'improves clinical accuracy, factual consistency, and interpretability' on CT-RATE and RadChestCT is presented without any numerical metrics, confidence intervals, statistical tests, or even the names of the evaluation metrics used. Because the performance gains constitute the primary empirical result, this omission prevents assessment of whether the improvements are substantive or statistically supported.

    Authors: We agree that the abstract must include concrete numerical results, metric names, confidence intervals, and statistical tests to allow immediate evaluation of the claimed improvements. In the revised manuscript we will expand the abstract to report the specific metrics used (e.g., clinical accuracy via RadGraph F1, factual consistency via entity-level F1, and interpretability via grounding scores), the observed percentage gains over the 2D/3D VLM baselines on both CT-RATE and RadChestCT, 95% confidence intervals, and p-values from paired statistical tests. revision: yes

  2. Referee: [—] Experiments section (assumed §4): the central mechanism depends on the pathology-specific diagnostic tools reliably extracting accurate localized volumetric features and on correct alignment of the multidimensional retrieval space with textual evidence. No precision, recall, or radiologist-agreement figures are supplied for tool outputs on the evaluation sets, nor is any ablation or alignment metric reported for the retrieval component. If either component underperforms, the grounding benefit and the reported superiority over direct VLMs would not follow.

    Authors: We acknowledge that the manuscript currently lacks independent quantitative validation of the diagnostic tools and retrieval alignment, which limits the ability to attribute end-to-end gains specifically to these components. We will add to the experiments section: (i) precision, recall, and radiologist agreement (Cohen’s kappa) for the pathology-specific tool outputs on the held-out evaluation sets; (ii) ablation results that isolate the contribution of the retrieval module; and (iii) retrieval-specific metrics such as recall@K and alignment scores between retrieved textual evidence and tool-extracted features. These additions will directly address the concern about component reliability. revision: yes

Circularity Check

0 steps flagged

No significant circularity in the procedural agentic workflow

full rationale

The paper describes MedScribe as a hypothesis-driven, iterative evidence-acquisition workflow in which an LLM invokes pathology-specific diagnostic tools, queries a retrieval space, and accumulates quantitative features before report synthesis. This is presented as a high-level procedural framework with empirical comparisons on CT-RATE and RadChestCT, not as a closed-form derivation, fitted-parameter prediction, or self-referential equation chain. No load-bearing self-citations, uniqueness theorems, ansatzes smuggled via prior work, or renamings of known results appear in the provided description. The central claims rest on the workflow's design and external dataset evaluations rather than any reduction of outputs to inputs by construction, rendering the argument self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework depends on unverified assumptions about tool accuracy and retrieval alignment that are not supported by independent evidence in the abstract.

axioms (2)
  • domain assumption Pathology-specific diagnostic tools can extract accurate localized volumetric features from 3D CT data.
    Invoked as the basis for evidence accumulation in the iterative decision process.
  • domain assumption The multidimensional retrieval space is aligned with pathology-specific textual evidence.
    Required for querying and grounding claims prior to report synthesis.

pith-pipeline@v0.9.0 · 5482 in / 1002 out tokens · 45537 ms · 2026-05-08T19:17:47.192865+00:00 · methodology

discussion (0)

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Reference graph

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