超选择性可解释性:在临床决策支持系统中对可解释性的实用性进行实证案例研究
Shaul A Duke1, Peter Sandøe1, Thomas Bøker Lund1
1University of Copenhagen, Copenhagen, Denmark.
概括
可解释的AI (人工智能) 对于早期癌症缓解症检测至关重要,但临床医生难以解释SHAP解释,限制了实际使用. 开发真正可解释的AI用于临床环境仍然是一个重大挑战.
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
- 可解释的人工智能 (XAI)
背景情况:
- 人工智能的"黑子"性质在临床应用中带来了挑战.
- 建议可解释性以增强对人工智能工具的信任和采用.
- 关于人工智能可解释性方法在医疗保健中的有效性的实证数据有限.
研究的目的:
- 评估AI决策支持工具可解释性场景的有效性.
- 评估临床医生对Shapley添加解释 (SHAP) 的理解和实用性,以检测与癌症相关的缓解症.
- 确定在临床实践中实施可解释的人工智能的挑战.
主要方法:
- 进行了13次采访,与专门从事缓冲症管理的临床医生进行了采访.
- 评估之前的临床医生使用人工智能工具的经验和对可解释性的看法.
- 使用SHAP方法提出了一个特定的可解释性场景.
主要成果:
- 大多数接受采访的临床医生之前的AI经验有限.
- 大多数人认为人工智能可解释性对于早期缓解症检测至关重要.
- 临床医生对SHAP特征的熟悉程度有限,只有核医学专家发现它们可能有用.
结论:
- 超选择性可解释性工具在临床环境中面临挑战.
- 对于临床医生来说,特定的SHAP解释的实际实用性有限.
- 为医疗保健开发可通过设计解释的人工智能系统需要进一步的研究.
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