探索基于概念的AI解释在胃肠道疾病检测中的临床价值
Andrea M Storås1,2, Maximilian Dreyer3, Frederik Pahde3
1Department of Holistic Systems, SimulaMet, Oslo, Norway. andreatheomine@gmail.com.
Scientific reports
|August 7, 2025
概括
可解释的人工智能 (AI) 帮助深度神经网络检测胃肠道异常. 目前的人工智能解释方法需要改进,以便在临床胃肠道学中使用,但对研究人员来说有价值.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 胃肠病学 胃肠病学
背景情况:
- 深度神经网络 (DNN) 在检测胃肠道异常,如多和瘤方面表现出高准确性.
- 在内镜中整合人工智能有助于胃肠科医生在早期发现疾病.
- 通过模型解释了解DNN的决策过程对于临床信任和发展至关重要.
研究的目的:
- 评估用于检测胃肠道异常的DNA的三个可解释的人工智能 (XAI) 方法.
- 收集胃肠病学家关于AI模型解释的临床适用性的反.
- 确定XAI在临床环境中的局限性和潜在改进.
主要方法:
- 探索三种不同的XAI技术,应用于胃肠道成像的DNN.
- 向胃肠病学家小组展示AI模型解释.
- 解释方法临床有用性的定性评估和用户反分析.
主要成果:
- 目前的XAI方法并不完全符合胃肠病学中直接临床应用的严格要求.
- 胃肠道学家发现解释对理解模型行为有用,尽管有局限性.
- XAI方法为人工智能研究人员和模型开发人员提供了宝贵的见解.
结论:
- 现有的XAI技术需要进一步改进,才能有效地集成到临床胃肠病学工作流程中.
- 提高数据集质量和优化解释的呈现是提高临床接受度的关键.
- 虽然XAI有望在医疗保健领域推进人工智能,但研究能力和临床需求之间仍然存在差距.
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