医学,心理学和社会科学中的AI和机器学习术语:教程和实践建议
Bo Cao1,2,3, Russell Greiner1,2,4, Andrew Greenshaw1
1Department of Psychiatry, University of Alberta, 4-142A Katz Group Centre for Research, 11315 - 87 Ave NW, Edmonton, AB, T6G 2B7, Canada, 1 7804929576.
Journal of medical Internet research
|August 18, 2025
概括
人工智能 (AI) 和机器学习术语在研究中经常被滥用. 本文澄清了关键术语,如"预测",以改善科学沟通和严格的AI应用在医学和社会科学中.
科学领域:
- 人工智能,机器学习,医学,心理学和社会科学领域的跨学科研究.
背景情况:
- 在AI和机器学习应用方面存在广泛的术语混.
- 像"预测"这样的关键词经常被滥用,通常应用于显示关联或回顾性分析而不是未来预测的研究.
研究的目的:
- 澄清关键AI和机器学习术语的正确使用.
- 为一致的术语提供基于证据的建议,以提高研究严谨性和公众的理解.
- 描述AI,机器学习,深度学习,大型语言模型和生成AI之间的等级关系.
主要方法:
- 从人工智能和机器学习术语的系统审查中对新出现的证据进行审查.
- 对诸如"预测"等术语的常见滥用情况进行分析.
- 在预测建模中解释验证,过拟和泛化概念.
主要成果:
- "预测"经常被错误地用于关联或回顾性研究; "前性预测"被推用于未来的预测.
- 讨论了确保模型通用性的基本验证程序.
- 在特征,独立变量,预测因素,风险因素和因果因素之间进行了明确的区分.
- 阐明了人工智能及其子领域的等级结构.
结论:
- 标准化术语对于跨学科的人工智能研究的清晰沟通至关重要.
- 准确使用诸如"预测"等术语和理解验证对于推进人工智能应用至关重要.
- 这项工作旨在促进人工智能在医学,心理学和社会科学领域的更严格和透明的使用.
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