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通过大型语言模型推进医疗保健:应用程序的范围审查和未来的方向

Zhihong Zhang¹, Mohamad Javad Momeni Nezhad², Seyed Mohammad Bagher Hosseini²

¹Data Science Institute, Columbia University, New York, NY 10027, United States; School of Nursing, Columbia University, New York, NY 10032, United States.

International journal of medical informatics

|December 24, 2025

概括

生成型大语言模型 (LLM) 在医疗保健方面表现有前途,有助于临床决策和患者信息. 进一步的研究和标准化的评估对于它们的整合和优化医疗保健服务至关重要.

科学领域:

医疗信息学医疗信息学
人工智能在医学中的应用
自然语言处理自然语言处理.

背景情况:

在ChatGPT发布后,生成性大语言模型 (LLM) 在医疗保健中得到了快速采用.
本范围审查系统地检查了医疗保健行业当前LLM利用的情况.

研究的目的:

系统地审查和分析生成LLM在医疗保健中的应用和性能.
识别LLM使用趋势,常见任务,数据集,模型和评估指标.

主要方法:

在PubMed上对2023年1月1日至2024年7月30日期间发表的研究进行了系统的文献搜索.
包括的研究评估了医疗保健应用中的生成LLM性能,不包括评论或视角文章.

主要成果:

包括415项研究,出版物数量在2023年4月以后急剧增加.
主要应用包括临床决策 (26.7%) 和患者信息 (23.9%),利用问题答案,文本分类和文本生成.
GPT-4,GPT-3.5和ChatGPT是最常见的LLM,直接提示是主要的适应方法. 准确度是被评估最多的指标,对错误,安全和时间效率的关注较少.

结论:

法学士课程为改善医疗服务,优化工作流程和医疗教育提供了巨大的潜力.
在研究和培训中扩大LLM应用可以推动创新.
标准化的评估标准对于安全有效地将LLM整合到医疗保健中至关重要.

关键词:

生物医学是生物医学.医疗保健医疗保健医疗保健医疗保健大型语言模型.自然语言处理语言处理

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