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人工智能检测统计错误:对作者,评论员和编辑的影响
Fatima Alnaimat1, Abdel Rahman Feras AlSamhori2, Husam El Sharu3
1Division of Rheumatology, Department of Internal Medicine, School of Medicine, University of Jordan, Amman, Jordan. f.naimat@ju.edu.jo.
Journal of Korean medical science
|December 23, 2025
概括
像Statcheck和GRIM-Test这样的人工智能 (AI) 工具通过识别统计错误来提高研究完整性,提高可靠性. 虽然人工智能在数据分析和同行评审方面提供了宝贵的支持,但人类监督对于准确性和负责任的使用至关重要.
科学领域:
- 研究诚信研究诚信
- 统计分析 统计分析
- 科学中的人工智能.
背景情况:
- 统计错误可能导致错误的研究结论,损害科学完整性.
- 研究完整性要求诚实,清晰的陈述和正确的统计方法.
- 人工智能 (AI) 系统正在成为检测统计错误和帮助研究人员的工具.
研究的目的:
- 评估AI在发现研究中的统计错误方面的作用和有效性.
- 探索人工智能工具如何帮助保持研究完整性和改善同行评审.
- 了解AI在科学研究的统计分析中的能力和局限性.
主要方法:
- 审查人工智能工具,如Statcheck,GRIM-Test,LLMs,Black Spatula和YesNoError用于统计错误检测.
- 分析人工智能在识别方法,引用和统计分析中的错误方面的表现.
- 在受控与复杂数据分析场景中评估AI准确性.
主要成果:
- 人工智能工具,特别是Statcheck和GRIM-Test,在发现统计错误方面表现有前途,提高了研究可靠性.
- 人工智能表现出适度的整体准确性,在受控设置中表现更好.
- 人工智能可以加快同行评审,减少审核员的工作负担,但有局限性,包括偏见和缺乏专家判断.
结论:
- 人工智能为研究完整性和统计准确性提供了有价值的,尽管不完美的支持,特别是随着撤回日益增加.
- 有效和安全的AI实施需要大量数据集,跨学科的协作和安全的系统.
- 人类监督对于最终决策不可或缺,确保在研究中负责任地利用人工智能.
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