使用大型语言模型对临床审查生成的横截面试验研究.
Zining Luo1,2,3,4,5, Yang Qiao6, Xinyu Xu5
1Department of Gastrointestinal Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
NPJ digital medicine
|March 20, 2025
概括
大型语言模型 (LLM) 可以快速生成临床评论,但与人类作者相比,缺乏深度和准确性. 目前的人工智能检测方法是不够的,突出了对医疗研究中负责任的人工智能整合的增强系统和伦理准则的需求.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 证据综合 证据综合
背景情况:
- 医学文献的快速增长需要有效的证据综合工具.
- 大型语言模型 (LLM) 显示出产生临床评论的前景.
- 对LLM产生的医学内容的可靠性和准确性存在担忧.
研究的目的:
- 系统地比较LLM产生的临床评论与人类撰写的评论.
- 评估人工智能生成的医学文献摘要的质量,准确性和引用.
- 在临床审查的背景下,评估当前人工智能检测系统的有效性.
主要方法:
- 由LLM和人类专家生成的临床评论的比较分析.
- 评估指标包括全面性,逻辑一致性,引用准确性和参考质量.
- 评估现有的AI产生的内容检测工具.
主要成果:
- 通过LLM生成的评论制作得更快,但含有更少的引用和更不全面的见解.
- 人工智能生成的内容表现出较低的逻辑一致性,真实性和引用准确性.
- 相当一部分LLM参考文献来自较低级别的期刊.
- 目前的人工智能检测系统在识别人工智能生成的临床评论方面表现出效率低下.
结论:
- 虽然LLM在生成评论方面提供了速度,但与人类作者相比,它们目前在质量和可靠性方面不足.
- 需要改进人工智能检测机制和强大的道德框架.
- 在临床研究中负责任地整合LLM需要解决准确性,透明度和验证方面的挑战.
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