在医疗保健中实施大型语言模型:以临床医生为中心的审查与交互式指导方针
HongYi Li1,2, Jun-Fen Fu3,4,5, Andre Python1,6,7
1Center for Data Science, Zhejiang University, Hangzhou, China.
Journal of medical Internet research
|July 11, 2025
概括
大型语言模型 (LLM) 可以帮助临床医生,但通用模型在专门的任务上扎. 本综述为选择适合特定临床应用的LLM提供了指导.
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) 具有临床应用的潜力,例如医疗问题回答和报告生成.
- 法律法规的快速发展给临床医生在选择合适的人工智能工具方面带来了挑战.
- 需要指导,以促进LLM有效地融入医疗保健机构.
研究的目的:
- 为临床医生提供系统指导,帮助他们选择适合他们的特定需求的LLM.
- 促进LLMs在临床工作流程中的整合.
- 为在医疗保健环境中应用LLM提供实用参考.
主要方法:
- 从2022年1月到2025年3月,在主要数据库 (PubMed,ScienceDirect,Scopus,IEEE Xplore) 和arXiv.com) 中对临床LLM应用进行了系统的文献搜索.
- 包括270项研究,重点关注创新的多式联络法学士的临床应用.
- 收集了330个LLM的数据,它们的应用频率和临床任务中的表现.
主要成果:
- 在临床任务中,LLM显示出实用性,特别是在5个阶段工作流程的第二,第三和第四阶段.
- GPT-3.5和GPT-4显示出多功能性,涵盖了显著比例的临床子任务.
- 一般目的的LLM通常需要针对专门的临床领域进行微调;多式联络LLM经常缺乏透明度,并引发数据隐私问题.
结论:
- 法律法规可以帮助临床医生,但缺乏适用于各种临床任务的通用模型,这给部署带来了挑战.
- 一项拟议的交互式在线指南旨在帮助临床医生根据特定的临床任务选择合适的LLM.
- 该指南是为临床使用而设计的,避免技术术语,以确保可访问性和成功的LLM应用.
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