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医疗保健中的自然语言生成:方法和应用的审查

Mengxian Lyu¹, Xiaohan Li¹, Ziyi Chen¹

¹Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA.

Journal of biomedical informatics

|February 12, 2026

概括

医疗保健中的自然语言生成 (NLG) 正在迅速推进,转换器模型主导了总结和文档等应用. 关键的挑战包括确保事实一致性和人工智能安全性,以实现更广泛的采用.

科学领域:

医疗信息学医疗信息学
人工智能的人工智能
自然语言处理自然语言处理.

背景情况:

自然语言生成 (NLG) 在医疗保健中越来越重要,用于报告总结和临床文档等任务.
该领域经历了显著的增长,从2018年到2024年出版物激增.

研究的目的:

系统地审查NLG在医学领域的方法和应用.
提供NLG在医疗保健中的定量和定性分析.
识别当前趋势,主导架构,评估指标和应用领域.

主要方法:

在主要的科学数据库 (PubMed,ACM,Web of Science等) 进行了全面的文献搜索. ) 的情况.
分析了2018年至2024年间发表的113篇相关研究文章.
研究根据数据模式,模型架构,评估指标和应用领域进行分类.

主要成果:

文本到文本生成 (65.5%) 是最常见的数据模式,以变压器为基础的编码器解码器模型占主导地位 (61.6%).
红色和蓝色是广泛使用的自动评估指标,随着越来越多的人类评估指标的采用,如利克特尺度.
关键应用包括总结,临床文档,医学对话和数据增强.

结论:

由于基于变压器的大型语言模型 (LLM) 和大型多模式临床数据集,NLG在医疗保健领域取得了重大进展.
持续的挑战包括事实一致性,可解释性,评估稳定性和AI安全性.
应对这些挑战对于成功将NLG技术纳入临床实践至关重要.

关键词:

临床文字临床文本医疗保健医疗保健医疗保健医疗保健大型语言模型.自然语言生成自然语言生成系统性审查系统性审查变压器模型模型

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