Liu Jialin, Liu Fang, Wang Changyu, Liu Siru
Department of Medical Informatics, West China Hospital, Sichuan University, Chengdu, China.
Department of Otolaryngology - Head and Neck Surgery, West China Hospital, Sichuan University, Chengdu, China.
J Med Internet Res. 2025 Sep 15;27:e72644. doi: 10.2196/72644.
Large language models (LLMs), such as OpenAI's GPT series and Google's PaLM, are transforming health care by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, as small changes in wording or structure can greatly impact output quality. This presents challenges for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot prompting, one-shot prompting, few-shot prompting, chain-of-thought prompting, self-consistency prompting, generated knowledge prompting, and meta-prompting. We provide actionable guidance on defining objectives, applying core principles, iterative prompt refinement, and integration into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.
大型语言模型（LLMs），如OpenAI的GPT系列和谷歌的PaLM，正在通过改善临床决策、加强医患沟通和简化管理任务来改变医疗保健行业。然而，它们的性能在很大程度上依赖于提示设计，因为措辞或结构上的微小变化可能会极大地影响输出质量。这给并非自然语言处理（NLP）专家的临床医生带来了挑战。本教程结合了针对临床使用量身定制的提示工程技术，涵盖了零样本提示、单样本提示、少样本提示、思维链提示、自一致性提示、生成知识提示和元提示等方法。我们提供了关于定义目标、应用核心原则、迭代提示优化以及集成到可互操作的电子健康记录（EHR）系统的可操作指导。该框架帮助临床医生利用大型语言模型来改善决策、简化文档记录并加强医患沟通，同时保持道德标准并确保患者安全。
