揭开医学的大型语言模型的神秘性:一本入门书
Qiao Jin1, Nicholas Wan1, Robert Leaman1
1National Library of Medicine (NLM), National Institutes of Health (NIH), Bethesda, MD, USA.
ArXiv
|January 13, 2025
概括
本文为医疗保健专业人员提供了一份指导方针,以有效地在临床环境中使用大型语言模型 (LLM). 它涵盖了任务制定,模型选择,快速工程和部署,以便在医学中安全有效地整合人工智能.
科学领域:
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
- 临床决策支持 临床决策支持
背景情况:
- 大型语言模型 (LLM) 在医疗保健中提供了变革性的潜力.
- 临床医学可以产生类似人类的反应,并适应新的医疗任务.
- 应用程序包括临床文档,患者试验匹配和医学问题答案.
研究的目的:
- 为医疗保健专业人员提供可操作的指导方针,以便有效地利用LLMs.
- 概述将LLM整合到临床工作流程中的最佳实践.
- 促进人工智能在医学中的安全,可靠和有影响力的应用.
主要方法:
- 提出了一个结构化,逐步的方法.
- 阶段包括任务制定,LLM选择,提示工程,微调和部署.
- 审查了将LLM适应专业医疗任务的考虑因素.
主要成果:
- 该指南针对了确定适合LLM的医疗保健任务的关键因素.
- 讨论了根据任务,数据和绩效选择合适的LLM的策略.
- 详细介绍了适应LLM的方法,包括快速工程和微调.
结论:
- 有效的LLM集成需要仔细的任务对齐和模型选择.
- 快速的工程和微调是专门用于医疗用途的LLM的关键.
- 部署必须解决监管合规,道德考虑和偏见监测.
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