在临床实践中,对大型语言模型进行有效的快速设计.
1Department of Internal Medicine & Infectious Diseases, Ghent University Hospital (UZ Gent), Ghent, Belgium.
Acta clinica Belgica
|January 12, 2026
概括
大型语言模型 (LLM) 增强了医疗保健,但有效使用需要快速工程. 掌握这种技能,包括检索增强生成 (RAG),对于安全的临床整合和提高准确性至关重要.
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
- 卫生信息技术 信息技术
背景情况:
- 大型语言模型 (LLM) 在临床文档,诊断和教育方面表现有前途.
- 有效的LLM利用取决于快速工程来管理性能,偏差和数据准确性.
- 了解快速工程对于在医疗保健中利用LLM至关重要.
研究的目的:
- 对临床环境中大型语言模型的快速工程的证据进行审查和综合.
- 确定优化医疗保健LLM绩效的关键原则和先进技术.
- 突出限制和未来的方向安全的LLM整合到临床实践.
主要方法:
- 通过结构化的PubMed搜索识别到2025年12月的研究的叙述性综述.
- 从2023年开始,包括同行评审的研究和系统性审查.
- 补充了制造商的基准和医学研讨会的见解.
主要成果:
- 有效的临床提示工程涉及角色定义,上下文,任务制定和输出规范.
- 结构化框架 (RTF,BRAIN) 和提取增强生成 (RAG) 等先进技术提高了LLM的准确性,减少了幻觉.
- 在医疗执照考试中,LLMs获得了72%的准确性,但模型信心往往与准确性相反相关,临床验证有限.
结论:
- 大型语言模型在医疗保健中提供了显著的效率提升,但需要仔细实施.
- 医生需要迅速的工程技能,结构化的框架,RAG策略和人类监督,以确保安全的临床使用.
- 快速的工程素养是医疗保健专业人员必不可少的新兴技能.
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