核医学中的解释性AI
Sune Holm1, Daria Ferrara2, Miriam Pepponi3
1Department of Food and Resource Economics, University of Copenhagen, Copenhagen, Denmark. suneh@ifro.ku.dk.
European journal of nuclear medicine and molecular imaging
|November 25, 2025
概括
可解释的人工智能 (AI) 对于临床医生无法独立验证的任务至关重要. 对于AI预测癌症缓解症,当输出不确定或发生错误时需要解释,以确保可靠的临床工具.
科学领域:
- 人工智能在医学中的应用
- 临床决策支持系统 临床决策支持系统
背景情况:
- 卡切西亚显著影响癌症患者的治疗结果.
- 预测性模型的缓解症越来越复杂.
- 人工智能决策的透明度需要越来越多.
研究的目的:
- 探索可解释AI (XAI) 的必要性,在一个专注于预测癌症缓解症的多学科项目中.
- 定义AI可解释性在临床环境中何时至关重要的标准.
主要方法:
- 多学科项目会议涉及医学,数据科学,社会学和哲学.
- 在不同的临床环境中讨论和分析AI可解释性要求.
主要成果:
- 用户可以执行/验证的人工智能任务与他们无法执行/验证的人工智能任务之间的区别.
- 确定了人工智能解释性不可或缺的场景.
结论:
- 人工智能解释对于超出用户验证能力的任务至关重要.
- 文档可靠性可能足以提供可验证的输出,但可解释性可以增强信任,特别是不确定性或错误.
- 临床医生,人工智能开发人员和利益相关者之间的密切合作是可靠人工智能在实践中的关键.
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