医学医生对人工智能使用解释性范式的理论评估
Lemuel Clark Velasco1,2,3, Wei-Tsong Wang1
1Department of Industrial and Information Management, College of Management, National Cheng Kung University, Tainan City.
Digital health
|October 9, 2025
概括
这项研究评估了解释医生采用人工智能 (AI) 的理论. 社会认知理论,自决理论和双重因素模型最好地解释了医疗保健中的AI接受和抵抗.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 医学中的行为科学.
背景情况:
- 医生在医学中采用人工智能 (AI) 遇到了不同的反应.
- 解释人工智能接受和抵抗的现有理论框架缺乏全面的评估.
- 需要对解释医生人工智能集成的范式进行系统评估.
研究的目的:
- 批判性地评估现有的理论,概念模型和框架.
- 确定他们解释AI医生接受和抵抗能力的能力.
- 确定在医疗保健中负责整合人工智能的强有力的理论范式.
主要方法:
- 使用T2P2标准 (理论,技术,专业,个人) 进行结构化的批判性检查.
- 在Scopus和Web of Science的系统搜索中,发现了28项使用21个范式的研究.
- 用评级级和解释性分析来评估范式,以确保对齐和连贯性.
主要成果:
- 社会认知理论,自我决定理论和双重因素模型展示了强大的解释能力.
- 这些模型有效地解释了影响人工智能采用的心理机制和结构因素.
- 其他范式在相关性,适用性和上下文特异性方面存在局限性.
结论:
- 现有的范式在解释医生AI使用的理论稳定性上有所不同.
- 需要整合性范式来解决认知-动机和社会技术因素.
- 结果指导研究人员,临床医生和决策者选择合适的AI整合理论.
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