\section{Discussion}\label{sec:discussion}
In this paper, we have demonstrated the effectiveness of the INFORM-CT agentic framework in managing incidental findings on abdominal CT scans by leveraging advanced LLM, VLM, and segmentation models.
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While INFORM-CT can effectively handle the logic and image processing operations needed for incidental findings, it is still limited by the capabilities of the underlying segmentation and VLM models used as base functions. Specifically, we expect that a VLM capable of better labeling fine details against local scan regions will further improve recommendation performance.

% In this study, we proposed a novel method for detecting incidental findings on abdominal CT scans by leveraging a checklist and medical guidelines. Our approach integrates a VLM with a Retrieval-Augmented Generation (RAG) framework, guided by a planner-executor system. The results demonstrate that our method achieves high accuracy in predicting risk levels for liver and kidney incidental findings, outperforming baseline models such as MERLIN and RadFM.

% The evaluation of multiorgan findings further highlights the robustness and generalizability of our approach. By incorporating guidelines from nine different organs, our method provides accurate risk assessments in a comprehensive clinical setting. The interpretability evaluation indicates that our model's outputs align closely with clinical guidelines, enhancing its utility in real-world scenarios.

% Despite these promising results, there are limitations to our study. The reliance on annotated datasets and the need for expert validation may limit the scalability of our approach. Future work should focus on expanding the dataset, improving the automation of guideline parsing, and exploring the integration of additional medical imaging modalities.

% Overall, our method shows significant potential for improving the detection and risk assessment of incidental findings in abdominal CT scans, contributing to more efficient and accurate clinical decision-making.