在医疗保健中推进可解释的人工智能:必要性,进展和未来方向
Rashmita Kumari Mohapatra1, Lochan Jolly1, Sarada Prasad Dakua2
1Department of Electronics and Telecommunication Engineering, Thakur College of Engineering & Technology, Kandivalli (E), Mumbai, 400101, Maharashtra, India.
Computational biology and chemistry
|July 31, 2025
概括
本研究回顾了可解释的人工智能 (AI) 方法,用于可靠的医疗保健应用. 它提供了可解释AI (XAI) 方法的分类,以指导临床医生和研究人员负责任地采用AI.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 精确的肝脏几何建模对于治疗规划至关重要,以尽量减少对周围组织和血管的伤害.
- 机器学习和计算机视觉技术提供自动化和有效的肝脏图像细分.
- 人工智能系统的模糊性阻碍了它们在关键医疗保健领域的采用.
研究的目的:
- 专注于医疗保健中机器学习方法的可解释性.
- 介绍文献综述和可解释AI (XAI) 方法的分类学.
- 作为该领域理论家和实践者的参考.
主要方法:
- 对可解释AI (XAI) 方法的系统文献审查.
- 在2019年至2023年期间发表的XAI方法的分析.
- 开发一个全面的XAI方法特征和方面分类学.
主要成果:
- 识别和分类各种可解释的AI (XAI) 技术.
- 为各种用户群体提供了XAI方法的结构化概述.
- 突出了可解释模型在医疗保健中可靠的人工智能的潜力.
结论:
- 可解释人工智能 (XAI) 对于人工智能在医疗保健中安全有效地整合至关重要.
- 彻底的验证,数据质量,交叉验证和监管对于可信的人工智能至关重要.
- 开发的分类学可以指导研究人员和从业人员选择和应用适当的XAI方法.
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