使用人工智能半自动评估随机对照试验的可信性:一个案例研究
Ling Shan Au1, Lizhen Qu2, Jeremy Nielsen1
1Department of Obstetrics and Gynaecology, Monash University, Melbourne, Victoria, Australia.
Journal of clinical epidemiology
|January 19, 2025
概括
像ChatGPT这样的大型语言模型可以帮助评估随机对照试验 (RCT) 的可靠性. 这种人工智能工具显示出加速研究完整性审查过程的潜力,改善系统审查的数据提取.
科学领域:
- 医学研究诚信 医学研究诚信
- 临床研究中的人工智能
- 基于证据的医学基于证据的医学.
背景情况:
- 随机对照试验 (RCT) 对基于证据的医学至关重要,但可能会受到伪造数据的损害.
- 评估RCT可信度对于防止临床指南中的错误信息和保护公共健康至关重要.
- 目前用于检测有问题的RCT的方法是低效和劳动密集的.
研究的目的:
- 探索大型语言模型 (LLM) 在加速RCT可信度评估方面的潜力.
- 为了证明ChatGPT (GPT-4o) 在使用TRACT检查清单和自动化数据提取来评估RCT的应用.
主要方法:
- 一个案例研究使用ChatGPT处理RCT PDF并应用随机对照试验可靠性 (TRACT) 检查清单.
- 该研究的重点是自动化数据表提取用于统计分析,以提高可信度评估.
- 概述了一个逐步的过程,并考虑了未来的改进.
主要成果:
- 聊天GPT成功完成了分配的任务,为TRACT检查清单项目提供了准确的答案,并有效地综合了信息.
- 对于TRACT项目,ChatGPT和人类评估之间观察到84%的协议率,大大加快了定性审查.
- 聊天GPT准确地提取和重组数据表,四分之三达到100%的准确性,方便进一步分析.
结论:
- 在半自动化RCT可信度评估方面,ChatGPT显示出前景,尽管目前需要用户提示.
- 进一步的开发旨在提高准确性并减少用户对大规模纸张分析的依赖.
- 最终的目标是完全自动化评估众多RCT的过程,根据初步发现,这似乎是可行的.
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