{"ID":2847166,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.00381","arxiv_id":"2511.00381","title":"VisionCAD: An Integration-Free Radiology Copilot Framework","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.","short_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 u...","url_abs":"https://arxiv.org/abs/2511.00381","url_pdf":"https://arxiv.org/pdf/2511.00381v1","authors":"[\"Jiaming Li\",\"Junlei Wu\",\"Sheng Wang\",\"Honglin Xiong\",\"Jiangdong Cai\",\"Zihao Zhao\",\"Yitao Zhu\",\"Yuan Yin\",\"Dinggang Shen\",\"Qian Wang\"]","published":"2025-11-01T03:29:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.HC\"]","methods":"[]","has_code":false}