放射学中的代理人工智能:从大型语言模型的演变到未来的临床整合
Bardia Khosravi1,2, Pouria Rouzrokh1,2, Tugba Akinci D'Antonoli3,4
1Department of Radiology and Biomedical Imaging, Yale University, New Haven, Conn.
Radiology. Artificial intelligence
|January 14, 2026
概括
自主代理系统超出了大型语言模型,通过整合记忆,知识检索和计算机使用,提供积极的临床援助. 这些人工智能系统可以协调复杂的临床工作流程,改变医疗保健的提供.
科学领域:
- 人工智能在医学中的应用
- 临床工作流程自动化
- 医疗保健技术转型 医疗保健技术转型
背景情况:
- 基础模型,特别是大型语言模型,已经启动了医疗保健转型.
- 该领域正在向主动,以目标为导向的临床援助的自主代理系统转变,超越被动信息检索.
研究的目的:
- 探索从被动信息检索到主动,以目标为导向的临床辅助的范式转变,使用自主代理系统.
- 概述医疗保健中代理人工智能的能力,工作流程和实施考虑因素.
主要方法:
- 代理人工智能系统利用持久记忆,医疗知识的检索增强生成和计算机使用功能.
- 多代理系统展示了整个放射学生命周期的任务协调工作流程 (层次,协作,顺序).
- 为逐步部署和安全提出了一个四阶段的实施路线图.
主要成果:
- 代理人工智能系统可以自主协调整个临床工作流程,从预先获取到初步报告生成.
- 在复杂的任务中,与单个代理方法相比,多代理系统显示出更高的性能.
- 成功部署需要解决复杂性,经济可持续性,网络安全,偏见和治理问题.
结论:
- 代理人工智能代表了一项重大进步,能够重塑放射学实践范式.
- 实施需要在确定性工作流程中仔细管理概率模型,并确保人类监督.
- 未来的成功取决于解决利益相关者的责任,并将人工智能能力与改善患者结果的临床责任性相结合.
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