[2603.00123] CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.00123 (cs) [Submitted on 23 Feb 2026] Title:CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers Authors:Yannian Gu, Xizhuo Zhang, Linjie Mu, Yongrui Yu, Zhongzhen Huang, Shaoting Zhang, Xiaofan Zhang View a PDF of the paper titled CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers, by Yannian Gu and 6 other authors View PDF HTML (experimental) Abstract:Recent advances in Large Vision-Language Models (LVLMs) have shown strong potential for multi-modal radiological reasoning, particularly in tasks like diagnostic visual question answering (VQA) and radiology report generation. However, most existing approaches for 3D CT analysis largely rely on static, single-pass inference. In practice, clinical interpretation is a dynamic, tool-mediated workflow where radiologists iteratively review slices and use measurement, radiomics, and segmentation tools to refine findings. To bridge this gap, we propose CT-Flow, an agentic framework designed for interoperable volumetric interpretation. By leveraging the Model Context Protocol (MCP), CT-Flow shifts from closed-box inference to an open, tool-aware paradigm. We curate CT-FlowBench, the first large-scale instruction-tuning benchmark tailored for 3D CT tool-use and multi-step reasoning. Built upon this, CT-Flow functions as a clinical orchestrator capable of decomposing complex natural language querie...