使用文字分析和可读性识别ChatGPT编写的患者教育材料
Silas Monje1, Sophie Ulene2, Alexis C Gimovsky3
1The Warren Alpert Medical School, Brown University, Providence, Rhode Island.
American journal of perinatology
|April 9, 2024
概括
简单的语法分析可以可靠地区分人工智能生成的文本和人类撰写的医学文章. 一个新的评分系统在区分ChatGPT生成的内容方面实现了94.1%的灵敏度和100%的特异性.
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.
背景情况:
- 像ChatGPT这样的人工智能 (AI) 文本生成器越来越多地用于医学.
- 区分人工智能生成的内容与人类撰写的文本对于医学准确性和完整性至关重要.
- 以前的研究表明,语法分析可以区分人工智能生成的文本.
研究的目的:
- 开发和验证一种用于识别人工智能产生的产科产品的方法.
- 为此目的评估使用简单语法分析的可行性.
主要方法:
- 聊天GPT生成了25篇关于产科主题的患者教育文章.
- 使用验证的评分系统,对人工智能生成的文章进行了可读性和语法分析.
- 基于Flesch-Kincaid得分,字符数和平均单词长度开发了一个新的评分系统.
- 该评分系统在17个由人工智能生成的新 ACOG 文章和7个由人类撰写的 ACOG 文章上进行了测试.
主要成果:
- 与人类撰写的文章相比,人工智能生成的文章的字符数量较少 (3,066与7,426) 和Flesch-Kincaid得分较低 (46与59).
- 新的评分系统区分了人工智能产生的文章和人类撰写的文章,具有94.1%的灵敏度和100%的特异性 (AUC 0.99).
- 在人工智能生成的文章中,平均单词长度更大 (5.3 vs 4.8 字).
结论:
- 简单的语法分析,包括可读性得分和文本特征,可以准确地识别当前人工智能生成的医学文本.
- 随着人工智能在医学中的整合不断增长,区分人工智能产生的内容的工具对于医疗保健利益相关者来说至关重要.
- 这项研究展示了一种可行且准确的方法来检测人工智能生成的产科内容.
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