探索可解释的AI技术用于医疗保健中的文本分类:一个范围审查
Ibrahim Alaa Eddine Madi1, Akram Redjdal1, Jacques Bouaud1
1Sorbonne Université, Université Sorbonne Paris Nord, INSERM, Laboratoire d'Informatique Médicale et d'Ingénierie des connaissances en e-Santé, LIMICS, Paris, France.
Studies in health technology and informatics
|August 23, 2024
概括
可解释的人工智能 (XAI) 方法提高了对人工智能 (AI) 在医学文本分类中的理解. 需要进一步的研究来评估这些技术与医疗保健专业人员,以提高信任和透明度.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 文本分类对于使用机器学习 (ML) 和深度学习 (DL) 组织医疗数据至关重要.
- 人工智能在医疗保健中的"黑盒子"性质引发了对模型解释性的担忧.
- 可解释AI (XAI) 提供了理解AI决策过程的方法.
研究的目的:
- 对应用到医学文本分类中的XAI技术进行范围审查.
- 识别和分类在这个领域使用的不同类型的XAI方法.
- 评估XAI在医疗保健中的评估和用户采用的当前状态.
主要方法:
- 在医学文本分类中对XAI应用的系统文献搜索.
- 确定XAI技术的分类,分为模型特定和模型不可知的方法.
- 对XAI的有效性和可用性报告的研究分析.
主要成果:
- 确定了XAI方法的两个主要类别:模型特定和模型不可知.
- 虽然开发人员表现出一些积极的接待,但与医疗终端用户的正式评估很少.
- 有限的研究存在于XAI的实际影响和临床医生的接受.
结论:
- 在人工智能驱动的医学文本分类中,XAI技术有望提高透明度.
- 有一个显著的需要更严格的评估涉及医疗保健专业人员.
- 进一步的研究对于建立信任和促进可解释AI在医疗保健中的采用至关重要.
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