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临床实践中的快速工程:临床医生的教程

Jialin Liu^1,2, Fang Liu^3,4, Changyu Wang^1,5

¹Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China.

Journal of medical Internet research

|September 16, 2025

概括

本教程指导临床医生在医疗保健中有效使用大型语言模型 (LLM). 它详细介绍了提示工程技术,以优化临床决策和患者沟通的LLM性能.

科学领域:

人工智能在医学中的应用
临床信息学临床信息学
自然语言处理自然语言处理.

背景情况:

大型语言模型 (LLM) 在医疗保健中具有变革性的潜力,影响临床决策,患者沟通和行政效率.
有效利用LLM非常依赖于快速设计,这给缺乏自然语言处理 (NLP) 专业知识的临床医生带来了挑战.

研究的目的:

为LLMs提供专门针对临床应用量身定制的快速工程技术提供全面的教程.
为临床医生提供可行的策略,以利用LLM来提高医疗保健服务.

主要方法:

探索各种提示工程方法,包括零射击,一射击,少数射击,思维链,自我一致性,生成知识和元提示.
关于定义目标,应用快速设计的核心原则和代改进过程的指导.
将LLM应用程序集成到可互操作的电子健康记录 (EHR) 系统中的策略.

主要成果:

临床医生可以通过战略性提示工程显著提高LLM输出质量.
该教程提供了一个结构化的框架,用于在临床环境中应用先进的提示技术.
实施指南有助于将LLM纳入现有医疗保健工作流程.

结论:

快速工程对于最大限度地利用LLM在医疗保健中的好处至关重要.
这一框架使临床医生能够利用LLM来改善决策,文档和患者参与.
在LLMs的临床应用中,遵守伦理标准和患者安全至关重要.

关键词:

在 GPT 中,GPT 必须是 GPT.临床实践中的临床实践人与人工智能的协作大型语言模型快速的工程迅速的工程

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