RadiFlow: AI agents reconstructing radiology workflow

ZHANG Mengmeng; CONG Fuze; WANG Jing; ZHANG Hui; LI Juanjuan; NI Qinghua; DING Xuanting; TIAN Yonglin; LYU Yisheng; XUE Huadan; WANG Fei-Yue

doi:10.11959/j.issn.2096-6652.202535

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RadiFlow: AI agents reconstructing radiology workflow

Papers and Reports | 更新时间：2025-11-18

- RadiFlow: AI agents reconstructing radiology workflow
- Chinese Journal of Intelligent Science and Technology Vol. 7, Issue 3, Pages: 396-407(2025)
- 作者机构：
  
  1.中国科学院自动化研究所多模态人工智能系统全国重点实验室，北京 100190
  2.中国科学院大学人工智能学院，北京 100049
  3.中国医学科学院北京协和医院放射科，北京 100730
  4.澳门科技大学创新工程学院，澳门 999078
  5.北京交通大学计算机科学与技术学院，北京 100044
  6.清华大学信息科学技术学院，北京 100084
  7.中国科学院自动化研究所复杂系统管理与控制国家重点实验室，北京 100190
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(82372051);The Science and Technology Development Fund, Macao SAR(0093/2023/RIA2;0145/2023/RIA3;0157/2024/RIA2);Beijing Natural Science Foundation(L222099);Capital's Funds for Health Improvement and Research(2024-2-4015);Sichuan Key-Area Research and Development Program(2024YFHZ0011)
- DOI：10.11959/j.issn.2096-6652.202535
  CLC： TP391
- Received：28 August 2025，
  
  Revised：2025-09-09，
  
  Accepted：12 September 2025，
  
  Published：15 September 2025
- 稿件说明：
移动端阅览
张濛濛,丛福泽,王静等.RadiFlow：AI Agents重构放射科工作流程[J].智能科学与技术学报,2025,07(03):396-407.

ZHANG Mengmeng,CONG Fuze,WANG Jing,et al.RadiFlow: AI agents reconstructing radiology workflow[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):396-407.
张濛濛,丛福泽,王静等.RadiFlow：AI Agents重构放射科工作流程[J].智能科学与技术学报,2025,07(03):396-407. DOI： 10.11959/j.issn.2096-6652.202535.

ZHANG Mengmeng,CONG Fuze,WANG Jing,et al.RadiFlow: AI agents reconstructing radiology workflow[J].Chinese Journal of Intelligent Science and Technology,2025,07(03):396-407. DOI： 10.11959/j.issn.2096-6652.202535.

摘要

随着现代医学的飞速发展和诊疗需求的日益增长，放射科作为临床诊断的核心部门，工作流程面临着多重挑战。这些挑战突出体现在多设备区域的复杂患者调度、人工操作导致的流程效率瓶颈、诊断辅助缺乏多模态临床信息综合推理能力以及报告撰写耗时等方面。提出了基于代理智能（agentic AI）的放射科全流程智能系统RadiFlow，构建了预约与患者管理智能体、影像采集协同智能体、分析与诊断推理智能体以及报告生成与解读智能体。通过智能体的协同，系统能够高效处理复杂患者调度与跨区域设备分流、实现影像采集标准化与智能容错、进行精准辅助诊断、自动化生成规范化报告并进行解读。本文为初步总结报告，仅通过案例研究，验证RadiFlow在提升放射科效率、诊断准确性、患者满意度及减轻医护负担方面展现出显著潜力，后续将基于此框架构建放射科多智能体系统，在实际场景中应用验证，以期为构建更智能、高效的放射科提供创新思路。

Abstract

With the rapid advancement of modern medicine and the increasing demand for clinical diagnosis and treatment

radiology

as a core department of clinical diagnostics

faces multiple challenges. These are particularly reflected in the complexity of patient scheduling across multi-device environments

workflow inefficiencies caused by manual operations

the lack of multimodal clinical information integration for diagnostic reasoning

and the time-consuming nature of report writing. To address these issues

RadiFlow

an end-to-end intelligent radiology workflow system based on Agentic AI

was proposed. The system was composed of four cooperative agents: scheduling agents

radiological examination agents

diagnostic reasoning agents

and report generation and interpretation agents. Through agents collaboration

RadiFlow can efficiently handle complex patient scheduling and cross-regional equipment allocation

achieve standardized and fault-tolerant imaging acquisition

provide accurate diagnostic assistance

and automatically generate and interpret standardized reports. However

this work remains a preliminary research report

validated through case studies. RadiFlow demonstrates significant potential for improving radiology efficiency

diagnostic accuracy

patient satisfaction

and reducing the workload of healthcare professionals. Future work will extend this framework to build a multi-agent radiology system for real-world deployment and validation

aiming to provide innovative insights into developing a more intelligent and efficient radiology practice.

关键词

Keywords

references

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