临床文本生成:我们还在吗?
Nicolas Hiebel1, Olivier Ferret2, Karën Fort3
11Université Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Numérique, Orsay, France; email: nicolas.hiebel@universite-paris-saclay.fr, aurelie.neveol@cnrs.fr.
Annual review of biomedical data science
|March 18, 2025
概括
生成型人工智能 (AI) 或大型语言模型可以创建生物医学文本. 本综述涵盖了用于临床应用的文本生成方法,评估和伦理考虑.
科学领域:
- 生物医学信息学 生物医学信息学
- 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:
- 生成型人工智能 (AI),特别是大型语言模型 (LLM),在生物医学领域越来越多地被采用.
- LLM用于各种文本处理任务,如分类,信息提取和决策支持.
研究的目的:
- 通过LLMs审查过去和现在的非结构化文本生成方法.
- 在没有参考文本的情况下讨论评估开放文本生成的方法.
- 探索AI驱动文本生成的临床应用和伦理考虑.
主要方法:
- 审查现有关于生成AI文本制作方法的文献.
- 对评估开放式文本生成技术的分析.
- 讨论与人工智能生成的生物医学文本相关的伦理影响和潜在风险.
主要成果:
- 生成型人工智能为生物医学领域生产非结构化文本提供了先进的功能.
- 与基于参考的方法相比,对开放文本生成的评估提出了独特的挑战.
- 临床应用包括临床笔记生成和合成数据创建,用于健康数据的二次使用.
结论:
- 使用人工智能的高质量,伦理设计的文本生成对生物医学应用具有重大前景.
- 意识到过度自信和对人工智能产出的偏见等风险对于负责任的实施至关重要.
- 需要进一步的研究,以确保在医疗保健中安全有效地部署生成人工智能.
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