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arxiv: 2512.14732 · v2 · submitted 2025-12-10 · 💻 cs.LG · cs.AI· cs.CV· eess.IV

INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT

Idan Tankel , Nir Mazor , Rafi Brada , Christina LeBedis , Guy ben-Yosef This is my paper

Pith reviewed 2026-05-16 22:52 UTC · model grok-4.3

classification 💻 cs.LG cs.AIcs.CVeess.IV
keywords incidental findingsabdominal CTLLMVLMagentic frameworkmedical imagingautomationguideline compliance
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The pith

LLM-generated scripts integrate VLMs to automate incidental findings management in abdominal CT scans.

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

The paper proposes a plan-and-execute framework where an LLM generates Python scripts to manage incidental findings in abdominal CT scans according to medical guidelines. These scripts direct vision-language models, segmentation tools, and image processing routines to detect, classify, and report findings. Experiments on a benchmark for three organs demonstrate the framework's superior accuracy and efficiency compared to pure VLM approaches in a fully automatic end-to-end process. A reader would care because this could standardize and speed up what is currently a manual, variable process in radiology. The approach aims to reduce time and improve precision in handling often benign but clinically significant findings.

Core claim

A novel framework leverages LLMs and VLMs in a planner-executor setup to automate incidental findings detection, classification, and reporting for abdominal CT scans, where the LLM generates executable Python scripts that the executor runs using VLMs and other models to perform guideline-based checks, outperforming existing pure VLM-based methods on a CT abdominal benchmark for three organs.

What carries the argument

The planner-executor agentic framework, in which the LLM planner creates Python scripts from predefined base functions to orchestrate VLMs, segmentation models, and image processing for guideline adherence.

If this is right

  • Automates incidental findings management in a fully automatic end-to-end manner for abdominal CT scans.
  • Outperforms pure VLM-based approaches in accuracy on a benchmark dataset for three organs.
  • Outperforms pure VLM-based approaches in efficiency.
  • Follows established medical guidelines through scripted checks.

Where Pith is reading between the lines

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

  • The framework could extend to other imaging modalities if similar base functions are defined.
  • Success depends on the LLM not hallucinating invalid code, which might be tested by varying the guidelines.
  • Integration with existing radiology workflows could reduce reporting variability across radiologists.

Load-bearing premise

The LLM planner can consistently generate correct and executable Python scripts that properly integrate the VLMs and models without introducing errors or hallucinations.

What would settle it

Running the system on the CT abdominal benchmark where the generated scripts produce incorrect detections or fail to execute, showing no improvement or errors compared to VLM baselines.

Figures

Figures reproduced from arXiv: 2512.14732 by Christina LeBedis, Guy ben-Yosef, Idan Tankel, Nir Mazor, Rafi Brada.

Figure 1
Figure 1. Figure 1: Overview of INFORM-CT pipeline. The framework consists of three [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Guideline Parsing Process Demonstration. The left panel presents [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Predictions of the INFORM-CT model for scans, adhering to ACR [PITH_FULL_IMAGE:figures/full_fig_p010_3.png] view at source ↗
read the original abstract

Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported following established guidelines. Traditional manual inspection by radiologists is time-consuming and variable. This paper proposes a novel framework that leverages large language models (LLMs) and foundational vision-language models (VLMs) in a plan-and-execute agentic approach to improve the efficiency and precision of incidental findings detection, classification, and reporting for abdominal CT scans. Given medical guidelines for abdominal organs, the process of managing incidental findings is automated through a planner-executor framework. The planner, based on LLM, generates Python scripts using predefined base functions, while the executor runs these scripts to perform the necessary checks and detections, via VLMs, segmentation models, and image processing subroutines. We demonstrate the effectiveness of our approach through experiments on a CT abdominal benchmark for three organs, in a fully automatic end-to-end manner. Our results show that the proposed framework outperforms existing pure VLM-based approaches in terms of accuracy and efficiency.

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 INFORM-CT, a plan-and-execute agentic framework that uses an LLM planner to generate Python scripts orchestrating VLMs, segmentation models, and image-processing routines to automatically detect, classify, and report incidental findings in abdominal CT scans according to organ-specific medical guidelines. Experiments on a benchmark for three organs are claimed to show outperformance over pure VLM baselines in accuracy and efficiency in a fully automatic end-to-end manner.

Significance. If the empirical claims hold after proper validation, the work could meaningfully advance automated medical imaging by showing how LLMs can compose reliable visual-analysis pipelines that follow structured clinical guidelines, potentially improving consistency and throughput in incidental-finding reporting. The agentic decomposition addresses a known limitation of standalone VLMs in handling multi-step guideline logic.

major comments (3)
  1. [Methods (Planner-Executor Framework)] The central superiority claim rests on the LLM planner emitting correct, executable Python scripts that integrate VLMs and segmentation without systematic hallucinations or runtime errors, yet no success-rate statistics, retry/verification loops, or ablation isolating planner failures from VLM performance are reported (Methods section on planner-executor framework and Results).
  2. [Results / Experimental Evaluation] The abstract and results assert outperformance in accuracy and efficiency on the three-organ CT benchmark, but supply no quantitative metrics, baseline definitions, error analysis, dataset size/sources, or statistical significance tests, rendering the data support for the claim unverifiable from the manuscript.
  3. [Discussion / Limitations] The weakest assumption—that the generated scripts reliably enforce organ-specific guideline checks without missing findings or spurious reports—is load-bearing for the end-to-end automation claim but receives no empirical validation or failure-mode analysis.
minor comments (2)
  1. [Abstract] The abstract would be strengthened by including at least one key numerical result (e.g., accuracy delta or runtime reduction) rather than a purely qualitative claim.
  2. [Methods] Notation for the predefined base functions available to the planner is introduced without an explicit table or appendix listing their signatures and constraints.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments correctly identify gaps in reporting and validation that we will address through revisions. We provide point-by-point responses below.

read point-by-point responses

  1. Referee: [Methods (Planner-Executor Framework)] The central superiority claim rests on the LLM planner emitting correct, executable Python scripts that integrate VLMs and segmentation without systematic hallucinations or runtime errors, yet no success-rate statistics, retry/verification loops, or ablation isolating planner failures from VLM performance are reported (Methods section on planner-executor framework and Results).

    Authors: We agree that the current manuscript lacks explicit statistics on planner reliability. In the revised version, we will add success-rate statistics for the LLM planner across the benchmark cases, describe the retry and verification mechanisms used to handle execution errors, and include an ablation study separating planner-induced failures from downstream VLM performance. These additions will directly support the superiority claim. revision: yes

  2. Referee: [Results / Experimental Evaluation] The abstract and results assert outperformance in accuracy and efficiency on the three-organ CT benchmark, but supply no quantitative metrics, baseline definitions, error analysis, dataset size/sources, or statistical significance tests, rendering the data support for the claim unverifiable from the manuscript.

    Authors: The referee is correct that the current version does not provide the requested quantitative details. The revised manuscript will include specific accuracy and efficiency metrics with numerical values, explicit baseline definitions, error analysis breakdowns, dataset size and source information, and statistical significance tests (e.g., p-values) to make all claims fully verifiable. revision: yes

  3. Referee: [Discussion / Limitations] The weakest assumption—that the generated scripts reliably enforce organ-specific guideline checks without missing findings or spurious reports—is load-bearing for the end-to-end automation claim but receives no empirical validation or failure-mode analysis.

    Authors: We acknowledge that the manuscript does not yet provide empirical validation of this assumption. In the revised discussion and limitations sections, we will add a dedicated failure-mode analysis, including quantitative results on missed findings and spurious reports, along with examples of how the framework handles edge cases in guideline enforcement. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical systems paper

full rationale

The paper presents an empirical framework for incidental findings management using LLMs and VLMs in a planner-executor setup. It contains no equations, mathematical derivations, fitted parameters, or self-referential definitions. Central claims rest on benchmark experiments for three organs rather than any reduction to inputs by construction. No load-bearing self-citations or uniqueness theorems are invoked. The LLM code-generation assumption is an implementation detail subject to empirical validation, not a circular step.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The framework rests on assumptions about LLM code-generation reliability and VLM accuracy for medical tasks rather than new mathematical constructs.

axioms (2)
  • domain assumption LLMs can generate correct and executable Python scripts for medical image analysis tasks based on guidelines
    Invoked in the planner component description.
  • domain assumption VLMs and segmentation models provide sufficient accuracy for incidental finding detection when scripted
    Core to the executor performance claim.

pith-pipeline@v0.9.0 · 5501 in / 1143 out tokens · 30720 ms · 2026-05-16T22:52:19.373939+00:00 · methodology

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Reference graph

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