[2512.02814] Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting

Computer Science > Artificial Intelligence arXiv:2512.02814 (cs) [Submitted on 2 Dec 2025 (v1), last revised 27 Feb 2026 (this version, v2)] Title:Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting Authors:Yongrui Yu, Zhongzhen Huang, Linjie Mu, Shaoting Zhang, Xiaofan Zhang View a PDF of the paper titled Radiologist Copilot: An Agentic Framework Orchestrating Specialized Tools for Reliable Radiology Reporting, by Yongrui Yu and 4 other authors View PDF HTML (experimental) 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 planning to support detailed visual reasoning, adopts strat...