临床决策支持系统中的可解释人工智能:方法,应用和可用性挑战的元分析
Qaiser Abbas1, Woonyoung Jeong2, Seung Won Lee2,3,4,5,6
1Department of Electrical Engineering, Institute of Space Technology, Islamabad 44000, Pakistan.
Healthcare (Basel, Switzerland)
|September 13, 2025
概括
临床决策支持系统 (CDSS) 中的可解释AI (XAI) 改善了诊断,但面临着采用障碍. 研究强调需要更好的评估,透明度和对医疗保健中负责任的人工智能的伦理考虑.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
背景情况:
- 人工智能 (AI) 集成到临床决策支持系统 (CDSS) 中,可以提高诊断,风险分层和治疗规划.
- 可解释性AI (XAI) 对于克服AI模型临床采用障碍至关重要.
- 目前的人工智能应用具有前景,但需要进一步开发才能广泛临床使用.
研究的目的:
- 系统地分析XAI方法在CDSS中的应用和挑战.
- 确定XAI中临床决策支持的当前趋势,差距和未来方向.
- 为负责和临床相关的AI在医疗保健中的实施提供建议.
主要方法:
- 2018年至2025年间发表的62项同行评审研究的系统元分析.
- 在不同的临床领域检查XAI方法:放射学,瘤学,神经学和重症监护.
- 专注于模型无意识的技术,如梯度加权类激活映射 (Grad-CAM) 和注意力机制.
主要成果:
- 无模型的XAI技术在成像和顺序数据任务中很普遍.
- 在用户友好的评估,方法透明度和道德考虑方面存在重大差距.
- 观察到缺乏关于解释忠实性,临床医生信任和现实世界的可用性研究.
结论:
- 在CDSS中的XAI对于推进医疗保健AI至关重要.
- 解决验证,设计和解释性方面的差距对于负责任的AI至关重要.
- 未来的人工智能解决方案必须是透明的,道德的,和临床相关的成功整合.
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