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在干预放射学中用于大型语言模型的快速工程.

Nicholas Dietrich1, Nicholas C Bradbury2, Christopher Loh2

  • 1Temerty Faculty of Medicine, University of Toronto, 1 King's College Cir, Toronto, Ontario, Canada M5S 1A8.

AJR. American journal of roentgenology
|May 7, 2025
PubMed
概括

快速工程优化人工智能 (AI) 和大型语言模型 (LLM) 进行干预放射学 (IR). 本指南详细介绍了有效的临床应用和未来进展的技术和最佳实践.

科学领域:

  • 人工智能在医学中的应用
  • 大型语言模型的临床应用.
  • 干预性放射学工作流程优化工作流程优化

背景情况:

  • 快速工程对于提高AI和LLM性能至关重要,特别是在高风险的医疗领域.
  • 人工智能输出的精度和可靠性对于安全有效的临床决策至关重要.
  • 干预放射学 (IR) 可以从结构化的AI输入中获益,以改善实践.

研究的目的:

  • 提供与干预放射学相关的快速工程技术的概述.
  • 在IR环境中展示各种提示策略的实际应用.
  • 讨论临床IR中生成AI的挑战和未来方向.

主要方法:

  • 探索关键的提示工程策略:零射击,少数射击,思想链,思想树,自我一致性和定向刺激提示.
  • 技术的插图与IR特定的例子用于工作场所和临床整合.
  • 讨论关于快速设计和分析临床生成AI挑战的最佳实践.

主要成果:

  • 证明了各种快速工程技术对IR任务的适用性.
  • 提供了在临床和工作场所环境中结构提示的实际例子.
  • 确定了关键挑战,包括数据隐私和对IR中生成AI的监管考虑.

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结论:

  • 有效的快速工程对于利用人工智能和LLM在干预放射学中至关重要.
  • 未来的进步,如提取增强生成和多式联运模式,有望进一步整合.
  • 应对挑战对于负责任和成功地在IR中采用生成AI至关重要.