医疗AI的光谱:不确定性,信任和后人类状态
1Chengdu University of Traditional Chinese Medicine, Chengdu, China li-huiemail@163.com.
Medical humanities
|January 6, 2026
概括
对医疗人工智能 (AI) 的信任受到不确定性的挑战. 本文介绍了一个不确定性地图,以将AI的使用与临床角色相协调,培养一种协作后人类护理模式.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 复杂性理论 复杂性理论
背景情况:
- 人工智能 (AI) 越来越多地融入临床实践.
- 这种整合引发了对用户信任和可靠性的重大担忧.
- 现有的信任框架可能无法充分解决医疗AI的复杂性.
研究的目的:
- 用复杂性理论分析对医学AI不信任的根源.
- 介绍一个概念工具,不确定性地图 (U-map),用于校准AI使用.
- 提出一个多维的信任框架和一个后人类的护理模式.
主要方法:
- 借鉴复杂性理论来理解AI相关的不确定性.
- 开发一个概念性的"U地图",将不确定性类型与临床角色 (选,分类,审议辅助) 联系起来.
- 构建一个包含可靠性,治理和社会文化因素的多维信任模型.
主要成果:
- 确定了医疗人工智能的多个不确定性来源:统计关系,系统复杂性和临床判断.
- U-map提供了一个工具,可以将AI能力与适当的临床功能相匹配.
- 制定了一个多维的信任框架,考虑技术,机构和文化方面.
结论:
- 解决对医疗人工智能的信任问题需要了解各种不确定性.
- 该U-map促进了AI在临床工作流程中的适当和安全整合.
- 提出了一个后人类护理模式,强调人机协作和分布式问责制,以应对AI的挑战.
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