Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting
Yongrui Yu, Zhongzhen Huang, Linjie Mu, Shaoting Zhang, and Xiaofan Zhang

TL;DR
Radiologist Copilot is an agentic framework that autonomously orchestrates specialized tools to perform the entire radiology reporting workflow, improving accuracy and efficiency over existing isolated report generation methods.
Contribution
It introduces a comprehensive, multi-stage system that integrates localization, interpretation, template selection, report composition, and quality control for radiology reporting.
Findings
Outperforms state-of-the-art methods in radiology reporting tasks
Supports the entire workflow from image analysis to report quality control
Demonstrates significant improvements in clinical alignment and reliability
Abstract
In clinical practice, radiology reporting is an essential yet complex, time-intensive, and error-prone task, particularly for 3D medical images. Existing automated approaches based on medical vision-language models primarily focus on isolated report generation. However, real-world radiology reporting extends far beyond report writing, which requires meticulous image observation and interpretation, appropriate template selection, and rigorous quality control to ensure adherence to clinical standards. This multi-stage, planning-intensive workflow fundamentally exceeds the capabilities of single-pass models. To bridge this gap, we propose Radiologist Copilot, an agentic system that autonomously orchestrates specialized tools to complete the entire radiology reporting workflow rather than isolated report writing. Radiologist Copilot enables region image localization and region analysis…
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Taxonomy
TopicsRadiology practices and education · Artificial Intelligence in Healthcare and Education · Multimodal Machine Learning Applications
