医学可解释的人工智能对护士创新行为的影响:结构方程建模方法
Xianmiao Li1, Qilin Zong1, Mengting Cheng2
1School of Economics and Management Anhui University of Science & Technology, Huainan, China.
Journal of nursing management
|April 14, 2025
概括
医学可解释的人工智能 (XAI) 通过提高人工智能自我效能和减少焦虑,促进护士的创新. 伦理组织气候进一步增强人工智能采用和护士创新.
科学领域:
- 护理信息学 护理信息学
- 医疗保健中的人工智能
- 人与计算机的交互
背景情况:
- 采用医疗人工智能技术对于解决医疗保健资源短缺和公共卫生需求至关重要.
- 提高护士的创新行为是医疗AI有效整合的关键.
- 可解释AI (XAI) 提供了一条改善AI在临床环境中的实际应用的途径.
研究的目的:
- 研究医学可解释的人工智能 (XAI) 对护士创新行为的影响.
- 探索AI自我效能和AI焦虑在XAI创新关系中的调解作用.
- 检查组织道德气候对医疗XAI影响的温和效应.
主要方法:
- 对368名中国三级和二级医院的护士进行了横截面调查.
- 数据来自安省,江苏省,江省和上海的护士.
- 进行了统计分析,以评估变量之间的关系.
主要成果:
- 医学XAI显著促进了护士的创新行为.
- 人工智能自我效能和人工智能焦虑在医疗XAI和护士的创新行为之间发挥了重要作用.
- 组织道德气候积极调节了医疗XAI和AI自我有效性之间的关联.
结论:
- 医学XAI通过增加AI自我效能和减少AI焦虑来增强护士的创新行为.
- 一个支持性的组织道德气候加强了医疗XAI和护士AI自我效能之间的积极联系.
- 研究结果强调了以人为中心的人工智能设计和针对护士的有针对性的培训的重要性.
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