医疗保健中的可解释AI:解释,预测或描述?
Alex Carriero1, Anne de Hond2, Bram Cappers3
1Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands. a.j.carriero@umcutrecht.nl.
Diagnostic and prognostic research
|December 4, 2025
概括
可解释的人工智能 (AI) 方法描述了AI模型的预测,但往往无法解释潜在的生物学原因. 这限制了它们用于可操作的医疗保健建议或验证AI模型的使用.
科学领域:
- 医疗保健中的人工智能
- 生物医学信息学 生物医学信息学
- 可解释的人工智能 (XAI)
背景情况:
- 可解释的人工智能 (AI) 方法旨在为医疗保健中的各种利益相关者阐明AI模型预测.
- 利益相关者对人工智能解释的需求各不相同,许多需要对生物机制的因果洞察.
研究的目的:
- 评估当前可解释AI (XAI) 方法在医疗保健中提供因果解释的能力.
- 评估XAI是否适合为患者,临床医生和开发人员提供信息.
主要方法:
- 通过描述-预测-解释框架分析可解释AI (XAI) 方法.
- 评估XAI揭示潜在生物机制和因果关系的能力.
主要成果:
- 可解释AI (XAI) 方法擅长描述AI模型操作,但在解释预测背后的"为什么"方面是有限的.
- 目前的XAI方法不能可靠地提供对生物过程的因果洞察.
结论:
- 可解释性AI (XAI) 方法主要是描述性的,而不是因果的,这限制了它们对可操作的医疗保健建议的有用性.
- 描述性和因果解释之间的差距阻碍了AI在医疗应用中的验证和信任.
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