医学机器学习的一个关键时刻:关于可重现和可解释的学习
Olga Ciobanu-Caraus1, Anatol Aicher1, Julius M Kernbach2
1Machine Intelligence in Clinical Neuroscience & Microsurgical Neuroanatomy (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland.
Acta neurochirurgica
|January 16, 2024
概括
医学中的机器学习 (ML) 面临着可重现性和可解释性危机. 解决方法严谨性和模型透明度对于临床采用和患者安全至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 机器学习 (ML) 出版物因计算进步而激增,但这种增长缺乏方法论严谨性,导致可重现性危机.
- 增加ML模型的复杂性阻碍了可解释性,阻碍了在患者安全至关重要的医疗保健中的临床采用.
研究的目的:
- 审查医学ML中可重现性和可解释性的挑战.
- 讨论解决方案,以提高医学ML模型的可靠性和临床适用性.
主要方法:
- 在医学ML中讨论可重现性和可解释性的语义.
- 概述潜在的解决方案,以解决ML模型的"黑子"性质.
- 强调报告准则,数据/代码共享和编辑标准的作用.
主要成果:
- 标准化报告准则,数据/代码共享和严格的同行评审对于可重复性至关重要.
- 简单的模型,模型不可知解释工具,灵敏度分析和隐藏层分析可以提高可解释性.
- 将模型性能与可解释性的平衡是临床整合的关键.
结论:
- 迫切需要采取行动,以解决医学中的ML可重现性和可解释性挑战.
- 实施解决方案将确保ML在医疗保健中的负责任和有效发展.
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