公式研究文章的爆炸,包括不适当的研究设计和错误的发现,基于美国国家卫生数据库NHANES
Tulsi Suchak1, Anietie E Aliu1, Charlie Harrison2
1School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, United Kingdom.
PLoS biology
|May 8, 2025
概括
人工智能 (AI) 通过像NHANES这样的数据集增强了健康研究,但有可能被造纸厂利用. 本研究确定了单因素分析和选择性数据使用的问题,并提出了确保研究完整性的最佳实践.
科学领域:
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
- 研究中的人工智能.
背景情况:
- 像国家健康和营养检查调查 (NHANES) 这样的AI准备数据集的增长提供了研究机会.
- 人工智能驱动的研究也带来了风险,包括纸厂的利用和数据完整性的损害.
- 担忧包括公式单因素分析和选择性数据使用,破坏了强大的科学调查.
研究的目的:
- 使用大数据集识别和解决与人工智能支持的研究相关的风险.
- 评估NHANES衍生出版物中单因素分析和选择性数据使用的普遍性.
- 为研究人员,数据控制者,出版商和审稿人提出最佳实践,以减轻人工智能辅助工作流程带来的风险.
主要方法:
- 在过去十年中,对NHANES数据的单因素分析进行系统的文献搜索.
- 对已识别的论文进行分析,以考虑多因素关系,纠正错误发现和数据选择理由.
- 审查出版趋势,并指出人工智能辅助的生产力显著增加.
主要成果:
- 在过去十年中,确定了341篇来自NHANES的研究论文.
- 发现了未能考虑多因素关系和错误发现风险的证据.
- 观察到从NHANES提取选择性数据,而不是使用完整的数据集,在2024年出版物急剧增加.
结论:
- 使用像NHANES这样的大型数据集进行人工智能辅助的研究需要谨慎的统计实践,以避免被造纸厂利用.
- 解决单因素分析,选择性数据使用和缺乏多因素考虑是至关重要的.
- 实施建议的最佳实践可以提高研究完整性,并打击低质量的AI生成手稿的涌入.
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