可读性评估和大型语言模型生成的临床试验描述总结的可读性评估和比较临床试验.gov
Tzu-Chun Wu1, Hanniel Shih1, Anunita Nattam1
1University of Cincinnati, Cincinnati, OH, United States.
Studies in health technology and informatics
|August 8, 2025
概括
像ChatGPT这样的大型语言模型 (LLM) 可以提高临床试验.gov信息的可读性. 聊天GPT总结显示阅读水平较低,质量高于原始试验描述,有助于非专业人士的理解.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 临床试验沟通临床试验沟通
背景情况:
- 临床试验.gov提供关键的试验信息.
- 试验描述的可读性往往很差,阻碍了公众的理解.
- 传统的可读性测量 (TRM) 用于评估文本的复杂性.
研究的目的:
- 为了评估临床试验.gov试验信息的可读性.
- 将原始试验描述的可读性与大语言模型 (LLM) 生成的摘要进行比较.
- 评估LLM生成的摘要的质量.
主要方法:
- 使用TRMs.gov对ClinicalTrials.gov描述的评估所需读取水平 (RRL).
- 使用ChatGPT和精心调整的BART-Large-CNN (FBLC) 生成的摘要.
- 通过两个独立审稿人的评分来评估摘要质量.
主要成果:
- 原始的ClinicalTrials.gov描述超过了推的可读性水平.
- 通过ChatGPT生成的摘要显示RRLs明显较低.
- 聊天GPT摘要从审查者那里获得了更高的质量评分.
结论:
- 聊天GPT展示了创建高可读性和高质量的试验摘要的潜力.
- 临床临床试验 (LLM) 可能提供一种可行的解决方案,以提高临床试验.gov的可读性.
- 需要进一步的研究来证实LLM在改善公众对临床试验数据的理解方面的实用性.
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