在谦虚地捍卫医疗保健中无法解释的黑子预测模型
Florien S van Royen1, Hilde J P Weerts2, Anne A H de Hond3
1Department of General Practice & Nursing Science, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands.
Journal of clinical epidemiology
|October 12, 2025
概括
可解释的人工智能 (XAI) 可能不适合医疗预测模型,可能导致误解和错误信任. 严格的验证,而不是XAI,应该建立对医疗AI工具的信心.
科学领域:
- 医疗保健 医疗保健 医疗保健 医疗保健
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 越来越复杂的医疗预测模型,包括使用人工智能 (AI) 的模型,需要透明度.
- 解释性AI (XAI) 技术经常被提议用于解释这些"黑子"模型.
研究的目的:
- 在医疗环境中批判性地评估XAI的实用性和局限性.
- 为建立对医疗预测模型的信任提出一个替代框架.
主要方法:
- 在医疗决策中XAI局限性的概念分析.
- 为优先考虑模型验证和影响研究而不是可解释性的论证.
主要成果:
- XAI可能不会产生临床相关的见解,并可能导致错误的信任.
- 难以避免XAI的因果解释可能导致确认偏见或不合理的解雇.
- XAI的解释可能与现有的临床知识相冲突,阻碍了采用.
结论:
- 对医疗预测模型的信任应该源自强有力的验证和影响评估,而不仅仅来自XAI.
- "无法解释的"但经过严格验证的模型可能更可靠用于医疗应用.
- 焦点应该从模型的可解释性转移到共享决策的可证明有效性.
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