人工智能在代表性不足群体的息/临终关怀中采用的机会和障碍:基于技术接受模型的审查
概括
人工智能 (AI) 可以通过解决文化和语言障碍来改善代表性不足的群体 (URG) 的宿舍和息护理. 然而,人工智能采用面临着诸如数据偏差和基础设施限制等挑战,需要进一步开发以获得公平的访问.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗保健中的人工智能
- 健康 公平 卫生 公平
背景情况:
- 由于文化,语言和系统因素,代表性不足的群体 (URG) 面临着获得临终关怀和息护理的重大障碍.
- 人工智能 (AI) 提出了潜在的解决方案,以增强文化敏感性,沟通和为URG提供个性化的护理.
- 技术接受模型 (TAM) 为了解医疗保健环境中人工智能采用提供了一个框架.
研究的目的:
- 综合现有的关于人工智能在宿舍和息护理中的应用文献.
- 检查AI在克服障碍和改善URG护理获取方面的潜力.
- 通过技术接受模型 (TAM) 的镜头来评估AI的作用.
主要方法:
- 使用基于Arksey和O'Malley框架的范围审查方法.
- 通过对9个数据库进行系统搜索,发现了关于URG临终关怀/息护理中人工智能的相关研究.
- 通过使用TAM框架进行叙事审查,选择并综合了17项研究.
主要成果:
- 人工智能工具在改善决策,及时转诊和弥合息治疗中的语言/文化差距方面显示出潜力.
- 人工智能证明了预测准确度的提高,加强了严重疾病的沟通,并解决了URG的语言障碍.
- 确定的障碍包括有限的概括性,数据偏差和基础设施挑战,这些障碍阻碍了在临终关怀中采用AI.
结论:
- 人工智能通过增强文化敏感性和沟通来提高URG的宿舍护理的变革潜力.
- 解决数据偏差,基础设施限制和文化细微差别对于实现人工智能的全部潜力至关重要.
- 未来的研究必须专注于开发具有文化能力,透明,可解释和可扩展的AI工具,以获得公平的护理.
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