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Updated: Jul 15, 2025

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用大型语言模型生成写作的临床研究-用人工智能辅助写作进行临床研究 (CRAW) 研究
Ivan A Huespe1,2, Jorge Echeverri3, Aisha Khalid4
1Hospital Italiano de Buenos Aires, Buenos Aires, Argentina.
Critical care explorations
|October 5, 2023
概括
生成预训练型变压器 (GPT) -3.5可以写出与人类专家无法区分的重症监护研究背景. 产生的GPT-3.5含量被评为比经验丰富的医学研究人员更高.
科学领域:
- 医学研究 医学研究
- 医学中的人工智能
- 科学写作 科学写作
背景情况:
- 在科学写作中讨论了生成预训练变压器 (GPT) -3.5的质量和伦理使用.
- 评估人工智能在产生研究内容中的作用对于学术诚信至关重要.
研究的目的:
- 为了比较由 GPT-3.5 与人类研究人员生成的重症监护研究问题的背景部分的质量.
- 评估医学研究人员区分人文和人工智能生成的科学文本的能力.
主要方法:
- 一项涉及来自20个国家的80名研究人员的观察性,横截面研究.
- "败血症中的急性损伤"的背景部分是由GPT-3.5和两组人类研究人员 (H指数>20和>10) 生成的.
- 参与者在5分利克特级别上对背景组件进行了评分,并试图识别人工智能生成的文本.
主要成果:
- 与具有22和13的H指数的研究人员撰写的背景相比,GPT-3.5生成的背景在关键组件上获得了更高的评分 (中位数为4.5对3.82和3.6).
- 检测GPT-3.5生成文本的能力很差,敏感度低 (22.4%) 和特异性低 (57.6%).
- 在利克特级别排名中观察到一个显著的差异 (p < 0.001).
结论:
- GPT-3.5可以产生与医学研究人员撰写的内容无法区分的背景研究内容.
- 在这个特定的背景下,人工智能生成的内容被评为高于经验丰富的医学研究人员的内容.
- 这些发现强调了人工智能工具在科学写作中的潜力,以及检测人工智能产生的内容的挑战.
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