为什么概率临床模型无法在网站之间传输?
Thomas A Lasko1, Eric V Strobl2, William W Stead2
1Vanderbilt University Medical Center, Nashville, TN, USA. tom.lasko@vanderbilt.edu.
NPJ digital medicine
|March 1, 2024
概括
医疗保健中的人工智能 (AI) 在新的临床环境中往往表现不佳. 这项研究解释了为什么人工智能模型无法传输,并建议解决方案,以提高它们在不同站点的可靠性.
科学领域:
- 医疗保健人工智能的人工智能
- 临床信息学 临床信息学
- 机器学习在医学中的应用
背景情况:
- 人工智能 (AI) 模型在医疗保健环境中显示出高性能.
- 当这些模型在新的临床环境中部署时,就会出现重大挑战,导致性能下降.
- 这种被称为传输失败的现象是临床AI采用的一个关键问题.
研究的目的:
- 解释AI模型在医疗保健中的运输失败背后的原因.
- 确定这种失败的来源,将其分类为实验者控制的因素和数据固有的因素.
- 提出一种方法,以减轻特定地点的临床实践对AI模型性能的影响.
主要方法:
- 该研究提出了关于运输失败现象的观点.
- 它分析了常见的失败来源,区分实验和固有的临床数据生成过程.
- 提出了一个潜在的解决方案,以隔离临床实践对数据的影响.
主要成果:
- 在一个临床环境中训练的AI模型通常在其他环境中表现不佳.
- 无法运输是常见的和预期的结果.
- 特定地点的临床实践显著改变了数据分布,影响了模型的通用性.
结论:
- 在医疗保健中运输AI模型的失败是一个可预测的问题.
- 了解和解决特定站点的数据变化对于可靠的AI部署至关重要.
- 提出的解决方案旨在将临床实践的影响与疾病模式分开,以获得更强大的AI模型.
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