干预放射学中大型语言模型的高级提示工程:实用策略和未来前景
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
|November 12, 2025
概括
先进的提示策略增强了干预放射学 (IR) 工作流程的大型语言模型 (LLM). 本指南详细介绍了在临床实践中安全,有效和基于证据的LLM整合的五种方法.
科学领域:
- 医学成像和干预性放射学
- 人工智能在医学中的应用
- 临床工作流的优化 临床工作流优化
背景情况:
- 大型语言模型 (LLM) 在临床环境中越来越多地被采用.
- 先进的提示技术对于优化LLM在干预放射学 (IR) 等专业领域的性能至关重要.
研究的目的:
- 呈现一个结构化的指南,五个先进的提示策略,LLMs在IR.
- 为了展示每个提示方法的实际IR特定用例.
- 概述LLM集成在IR中的要求,考虑和限制.
主要方法:
- 验证链 - 验证链
- 密度链-密度链-密度链
- 推理和行动 推理和行动
- 生成的知识提示提示.
- 提取增强生成的提取.
主要成果:
- 展示了提示如何引导LLM产生透明的,针对患者的和基于证据的响应.
- 概述了技术要求,临床考虑和限制,包括对抗性诱导风险.
- 探讨了新兴方向,如代理工作流程和对IR特定基准的需求.
结论:
- 高级提示为安全有效的LLM集成在高风险的IR工作流程中提供了基础.
- 强调需要人为循环设计,放射学特定的基准和监管标准.
- 为参与医学AI的临床医生,研究人员和开发人员提供价值.
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