检测和纠正有害数据转移,以负责任地部署临床AI模型
Vallijah Subasri1,2,3, Amrit Krishnan2, Ali Kore2
1Peter Munk Cardiac Centre, University Health Network, Toronto, Ontario, Canada.
JAMA network open
|June 4, 2025
概括
这项研究开发了一种监测系统,用于检测临床AI中的数据转移,使用转移和持续学习来保持患者死亡率的预测准确性. 这些方法确保了医疗保健机构的强大AI性能.
科学领域:
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
- 机器学习用于医疗保健
背景情况:
- 临床人工智能 (AI) 系统可能会因数据转移而降低性能,从而可能对患者造成伤害.
- 积极检测和减轻数据转移对于人工智能在医疗保健中的安全性和有效性至关重要.
研究的目的:
- 开发和评估一个主动的,无标签的监测管道,以检测和减轻临床AI中有害的数据转移.
- 评估转移学习和持续学习策略,以维持人工智能模型的性能.
主要方法:
- 利用来自7家大型医院 (2010-2020年) 的电子健康记录数据进行预后研究.
- 采用了无标签的监测管道,包括黑移位估计和最大平均差异测试.
- 使用AUROC和AUPRC评估住院死亡率的预测性能.
主要成果:
- 检测到与医院类型,患者人口统计和实验室检测相关的显著数据转移.
- 转移学习增强了社区医院的模型性能 (Delta AUROC: 0.05).
- 在COVID-19大流行期间,漂移触发的持续学习改善了整体模型性能 (Delta AUROC: 0.44).
结论:
- 一个主动监测管道成功检测到临床AI死亡预测系统中的有害数据转移.
- 转移学习和持续学习缓解了人工智能性能恶化,确保了在各种医疗保健环境中强大的性能.
- 这些发现支持对临床人工智能模型的可靠和公平部署.
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