放射学中的代理系统:原则,机会,隐私风险,监管和可持续性问题
Eleftherios Tzanis1, Lisa C Adams2, Tugba Akinci D'Antonoli3
1Artificial Intelligence and Translational Imaging (ATI) Lab, Department of Radiology, School of Medicine, University of Crete, 70013 Heraklion, Greece.
Diagnostic and interventional imaging
|December 23, 2025
概括
代理系统将大型语言模型 (LLM) 与放射学中自主行动的工具集成在一起. 本综述探讨了它们在医疗成像中增强人工智能工作流程的潜力,解决当前的整合挑战.
科学领域:
- 放射学中的人工智能
- 医学成像信息学 医疗成像信息学
- 计算病理学计算病理学
背景情况:
- 基于变压器的大型语言模型 (LLM) 显示出对自动化放射学任务,如报告生成的承诺.
- 传统的LLM受限于它们无法与外部系统交互,阻碍了临床工作流集成.
- 代理系统通过在框架内嵌入LLM来提供新的范式,使推理,规划和行动成为可能.
研究的目的:
- 为医疗成像和放射学中代理系统提供全面的概述.
- 总结放射学中代理系统的关键发展和应用.
- 讨论将代理系统集成到临床实践中的潜在好处和挑战.
主要方法:
- 对医疗成像中的代理系统和LLM现有文献的审查.
- 对代理系统架构和操作机制的分析.
- 对自动化放射学管道的多代理框架的检查.
主要成果:
- 代理系统扩展了LLM功能,可以与用户,工具和数据源进行动态交互.
- 最近的多代理框架显示了自动化放射学管道的潜力.
- 这些系统可以提高人工智能驱动的放射学工作流程的可复制性,可解释性和可访问性.
结论:
- 代理系统代表了人工智能在放射学方面的重大进步,使得更复杂的自动化和决策支持成为可能.
- 解决监管,伦理和可持续性挑战对于安全和负责任的临床整合至关重要.
- 需要进一步的研究来弥合现有的差距,以便在医学成像中广泛采用代理AI.
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