生成型人工智能和非生成型预测分析的统计数据 医学中的机器学习
Hooman H Rashidi1, Bo Hu2, Joshua Pantanowitz3
1Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania; Computational Pathology and AI Center of Excellence, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania.
概括
本综述比较了医学中生成性AI和传统机器学习 (ML) 的统计指标. 了解这些指标可以确保在医疗保健中负责任和科学合理的AI/ML应用.
科学领域:
- 医学 医学 医学 医学 医学
- 人工智能的人工智能
- 机器学习 机器学习
- 统计 统计 统计 统计
背景情况:
- 人工智能 (AI) 和机器学习 (ML) 在医学中的整合需要对它们的统计基础有很强的理解.
- 病理学和医学产生了适合AI/ML应用的大量数据,生成性AI,特别是大型语言模型,成为变革性工具.
研究的目的:
- 提供用于生成AI和传统 (非生成预测分析) ML的统计措施的概述和比较分析.
- 突出这些医疗应用的统计方法的优点和局限性.
主要方法:
- 在生成AI中常用的统计指标的审查 (例如,困惑度,双语评价Understudy得分).
- 对传统ML中用于分类 (例如准确度,灵敏度,F1得分,AUC) 和回归 (例如RMSE,R2) 的统计措施的审查.
- 确定统计措施的比较分析,重点关注它们在医疗环境中的适用性和局限性.
主要成果:
- 生成型人工智能利用独特的指标,如困惑度和双语评估理解得分来评估样本质量,这往往是医疗从业者不熟悉的.
- 传统的ML采用了更为熟悉的指标,如准确性,灵敏度,F1得分,AUC用于分类,RMSE,R2用于回归.
结论:
- 了解生成式和传统的ML之间的统计措施的相似性和差异对于医疗专业人员至关重要.
- 根据这些统计原则的明智应用将使AI/ML在医学中的负责任和科学合理的进步成为可能,解决当前和未来的挑战.
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