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

TL;DR

CT-Flow introduces an agentic framework for 3D CT interpretation that enables dynamic, tool-mediated reasoning, significantly improving diagnostic accuracy and autonomous tool use in clinical workflows.

Contribution

This work presents CT-Flow, a novel open, tool-aware interpretation framework utilizing the Model Context Protocol, and introduces CT-FlowBench, a large-scale benchmark for multi-step 3D CT reasoning.

Findings

Achieves 41% higher diagnostic accuracy than baseline models

95% success rate in autonomous tool invocation

Outperforms existing methods on CT-FlowBench and 3D VQA datasets

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 queries into automated tool-use sequences. Experimental evaluations on CT-FlowBench and standard 3D VQA datasets demonstrate that CT-Flow achieves state-of-the-art performance, surpassing baseline models by 41% in diagnostic accuracy and achieving a 95% success rate in autonomous tool invocation. This work provides a scalable foundation for integrating autonomous, agentic intelligence into real-world clinical radiology.