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CT-Flow: Orchestrating CT Interpretation Workflow with Model Context Protocol Servers
arXiv – CS AI|Yannian Gu, Xizhuo Zhang, Linjie Mu, Yongrui Yu, Zhongzhen Huang, Shaoting Zhang, Xiaofan Zhang||3 views
🤖AI Summary
Researchers have developed CT-Flow, an AI framework that mimics how radiologists actually work by using tools interactively to analyze 3D CT scans. The system achieved 41% better diagnostic accuracy than existing models and 95% success in autonomous tool use, potentially revolutionizing clinical radiology workflows.
Key Takeaways
- →CT-Flow introduces an agentic framework for 3D CT scan interpretation that mirrors real clinical workflows using the Model Context Protocol.
- →The system achieved 41% improvement in diagnostic accuracy compared to baseline models on standard benchmarks.
- →CT-Flow demonstrated 95% success rate in autonomous tool invocation for clinical analysis tasks.
- →CT-FlowBench represents the first large-scale benchmark specifically designed for 3D CT tool-use and multi-step reasoning.
- →The framework shifts from static single-pass inference to dynamic, tool-mediated workflows similar to how radiologists actually work.
#artificial-intelligence#medical-ai#computer-vision#healthcare#radiology#ct-scans#lvlm#clinical-workflow#diagnostic-accuracy#medical-imaging
Read Original →via arXiv – CS AI
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