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Updated: Jan 22, 2026

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在人工智能驱动的心电图解读中映射算法偏差 跨越人工智能生命周期:范围审查协议
Luqman Lawal1,2, Christopher Paton1,3, Mike English1
1Nuffield Department of Medicine, University of Oxford, Oxford, England, United Kingdom.
JMIR research protocols
|January 20, 2026
概括
本范围审查映射了人工智能 (AI) 驱动的心电图 (ECG) 分析中的算法偏差. 它确定了偏差和缓解策略的来源,以确保所有患者的公平心脏诊断.
科学领域:
- 心脏病学 心脏病学
- 人工智能的人工智能
- 医学诊断 医学诊断 医学诊断
背景情况:
- 在心电图 (ECG) 分析中的人工智能 (AI) 提供了先进的心脏诊断.
- 在AI-ECG工具中的算法偏差可能导致患者子组之间的性能差异.
- 没有解决的偏见可能会加剧健康不平等,特别是在代表性不足的人群中.
研究的目的:
- 系统地绘制AI启用心电图解释中的算法偏差的证据.
- 确定偏差对各个子组诊断绩效的来源,类型和影响.
- 在整个AI生命周期中记录偏见缓解策略.
主要方法:
- 根据PRISMA-ScR指导方针进行范围审查协议.
- 在5个数据库和灰色文献 (2015-2025) 中进行全面的文献搜索.
- 独立的研究选,数据提取和不同意见的共识.
主要成果:
- 搜索结果为430条记录;38项研究在全文审查后符合纳入标准.
- 目前正在进行确定研究的定性综合.
- 通过PRISMA流程图记录的研究选择过程.
结论:
- 这篇评论是第一个全面地绘制AI-ECG解释中的算法偏差.
- 它将确定不平等现象,并评估在心脏护理中的公平人工智能解决方案.
- 调查结果将为开发人员,临床医生和决策者提供有关促进人工智能公平性的信息.
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