VisionCAD: An Integration-Free Radiology Copilot Framework

• URL: http://arxiv.org/abs/2511.00381v1
• Date: Sat, 01 Nov 2025 03:29:50 GMT
• Title: VisionCAD: An Integration-Free Radiology Copilot Framework
• Authors: Jiaming Li, Junlei Wu, Sheng Wang, Honglin Xiong, Jiangdong Cai, Zihao Zhao, Yitao Zhu, Yuan Yin, Dinggang Shen, Qian Wang,
• Abstract summary: VisionCAD is a vision-based radiological assistance framework.<n>It captures medical images directly from displays using a camera system.<n>System achieves diagnostic performance comparable to conventional CAD systems.
• Score: 42.29535854844036
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Widespread clinical deployment of computer-aided diagnosis (CAD) systems is hindered by the challenge of integrating with existing hospital IT infrastructure. Here, we introduce VisionCAD, a vision-based radiological assistance framework that circumvents this barrier by capturing medical images directly from displays using a camera system. The framework operates through an automated pipeline that detects, restores, and analyzes on-screen medical images, transforming camera-captured visual data into diagnostic-quality images suitable for automated analysis and report generation. We validated VisionCAD across diverse medical imaging datasets, demonstrating that our modular architecture can flexibly utilize state-of-the-art diagnostic models for specific tasks. The system achieves diagnostic performance comparable to conventional CAD systems operating on original digital images, with an F1-score degradation typically less than 2\% across classification tasks, while natural language generation metrics for automated reports remain within 1\% of those derived from original images. By requiring only a camera device and standard computing resources, VisionCAD offers an accessible approach for AI-assisted diagnosis, enabling the deployment of diagnostic capabilities in diverse clinical settings without modifications to existing infrastructure.

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