使用ChatGPT改进简单语言简要介绍的表现 关于瘤干预的科克兰系统性评论:跨部分研究
Jelena Šuto Pavičić1, Ana Marušić2, Ivan Buljan3
1Department of Oncology and Radiotherapy, University Hospital of Split, Spinciceva 1, Split, 21000, Croatia, 385 2155817.
JMIR cancer
|March 19, 2025
概括
像ChatGPT这样的大型语言模型可以简化患者的医疗信息,提高Cochrane瘤学评论的可读性. 这种工具有助于使复杂的研究更容易获得,而不会改变证据结论.
科学领域:
- 医疗信息学 医疗信息学
- 瘤学研究研究
- 卫生沟通健康沟通
背景情况:
- 简单的语言摘要 (PLS) 对于瘤患者的决策至关重要.
- 当前的PLS格式往往缺乏一般人群所需的可读性.
- 需要用于PLS生成的自动化,具有成本效益的解决方案.
研究的目的:
- 评估像ChatGPT这样的大型语言模型 (LLM) 是否可以提高Cochrane瘤学PLSs的可读性.
- 评估LLM生成的PLS的语言特征和证据合成一致性.
主要方法:
- 275 柯克伦瘤学系统综述摘要和PLSs被使用.
- 聊天GPT-4使用三个不同的提示,以可读性和音调为重点,生成PLS.
- 分析了可读性 (SMOG指数),字数和语言特征,并与原始PLS进行了比较.
主要成果:
- 与原来的PLS (SMOG指数13.1) 相比,ChatGPT生成的PLS显著更短,更容易阅读 (SMOG指数8.2-8.7).
- 在LLM生成的摘要中,分析语调降低,情感语调和真实性增加.
- 最重要的是,关于干预疗效和安全的证据综合结论与原始摘要保持一致.
结论:
- 在简化复杂的医疗信息方面,LLM对普通人来说是有前途的.
- 需要进一步的研究和监督,以确保LLM产生的内容的准确性,可靠性和文化相关性.
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