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

Idan Tankel, Nir Mazor, Rafi Brada, Christina Lebedis, Guy Ben-Yosef
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:2461-2473, 2026.

Abstract

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

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-tankel26a, title = {INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT}, author = {Tankel, Idan and Mazor, Nir and Brada, Rafi and Lebedis, Christina and Ben-Yosef, Guy}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {2461--2473}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/tankel26a/tankel26a.pdf}, url = {https://proceedings.mlr.press/v315/tankel26a.html}, abstract = {Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported according to established guidelines. Traditional manual inspection by radiologists is time-consuming and subject to variability. This paper proposes a novel framework that leverages large language models (LLMs) and foundational vision–language models (VLMs) within a plan-and-execute agentic architecture to improve the efficiency and precision of incidental-findings detection, classification, and reporting in abdominal CT scans. Given medical guidelines for abdominal organs, the management process is automated through a planner–executor framework. The planner, based on an LLM, generates Python scripts from predefined base functions, while the executor runs these scripts to perform the required detections and evaluations using VLMs, segmentation models, and image-processing subroutines. We demonstrate the effectiveness of our approach through experiments on a CT-abdominal benchmark covering three organs, in a fully automatic end-to-end setup. Our results show that the proposed framework outperforms existing purely VLM-based approaches in both accuracy and efficiency.} }
Endnote
%0 Conference Paper %T INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT %A Idan Tankel %A Nir Mazor %A Rafi Brada %A Christina Lebedis %A Guy Ben-Yosef %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-tankel26a %I PMLR %P 2461--2473 %U https://proceedings.mlr.press/v315/tankel26a.html %V 315 %X Incidental findings in CT scans, though often benign, can have significant clinical implications and should be reported according to established guidelines. Traditional manual inspection by radiologists is time-consuming and subject to variability. This paper proposes a novel framework that leverages large language models (LLMs) and foundational vision–language models (VLMs) within a plan-and-execute agentic architecture to improve the efficiency and precision of incidental-findings detection, classification, and reporting in abdominal CT scans. Given medical guidelines for abdominal organs, the management process is automated through a planner–executor framework. The planner, based on an LLM, generates Python scripts from predefined base functions, while the executor runs these scripts to perform the required detections and evaluations using VLMs, segmentation models, and image-processing subroutines. We demonstrate the effectiveness of our approach through experiments on a CT-abdominal benchmark covering three organs, in a fully automatic end-to-end setup. Our results show that the proposed framework outperforms existing purely VLM-based approaches in both accuracy and efficiency.
APA
Tankel, I., Mazor, N., Brada, R., Lebedis, C. & Ben-Yosef, G.. (2026). INFORM-CT: INtegrating LLMs and VLMs FOR Incidental Findings Management in Abdominal CT. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:2461-2473 Available from https://proceedings.mlr.press/v315/tankel26a.html.

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