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为临床医生提供人工智能支持的机械通风优化解释用户界面:以用户为中心的设计和形成性的可用性研究.

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概括

为临床决策支持系统 (CDSS) 设计可解释的AI (XAI) 接口需要了解用户的需求. 这项研究发现,ICU护士和医生对机械通风支持中的AI解释有不同的偏好.

关键词:
这是一个CDSSCDSSCDSS.在ICU中,医生会对患者进行治疗.在XAI,XAI就是XAI.在XUIUI中,我们可以看到XUIUI.临床决策支持系统.可解释的人工智能解释用户界面用户界面形成性评价的形成性评价.重症监护病房的重症监护病房是一个重症监护病房.可用性可用性可用性

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科学领域:

  • 人工智能在医学中的应用
  • 人与计算机的交互
  • 临床决策支持 临床决策支持

背景情况:

  • 人工智能 (AI) 在机械通风的临床决策支持系统 (CDSS) 中的整合提供了潜力,但由于缺乏透明度而面临采用障碍.
  • 可解释的人工智能 (XAI) 和解释用户界面 (XUI) 对于在像重症监护室 (ICU) 这样的高风险环境中增强信任和可用性至关重要.
  • 对于临床医生与ICU中的XUI互动的理解有限,需要研究将AI无集成到临床工作流程中.

研究的目的:

  • 评估XUI的初始设计和用户对基于AI的CDSS的感知,以优化ICU中的机械通风.
  • 探索不同的临床用户群 (ICU护士和医生) 如何在XUI中感知和优先考虑解释概念.
  • 根据用户反和角色特定需求,收集经验数据来改进XUI设计.

主要方法:

  • 使用Justinmind开发了一个中保密XUI原型,坚持以用户为中心的设计 (UCD) 原则 (ISO 9241-210).
  • 通过两次与住院医生和ICU护士的可用性步行进行了形成性评估.
  • 收集定性和定量反,包括指导讨论和利克尔特尺度评估解释的可理解性,适合性和视觉吸引力.

主要成果:

  • 设计了一个两级的XUI:一级提供了高级解释 (异常值警告,输出确定性),二级提供了详细的见解 (输入,特征重要性,基于规则的解释).
  • 医生发现详细的第二层有用,而ICU护士更喜欢简洁的第一层,表明不同的角色特定需求.
  • 层层的设计有效地平衡了透明度和信息过载,证实了需要角色依赖的解释策略的需要.

结论:

  • 以用户为中心的设计 (UCD) 对于开发临床决策支持系统 (CDSS) 的有效解释用户界面 (XUI) 是至关重要的.
  • 识别和解决医生和ICU护士的独特信息需求对于XUI开发至关重要.
  • 结果为设计重症监护中的分层,角色敏感的XUI提供了实际指导,为未来对信任和决策的影响研究铺平了道路.