评估医疗保健利益相关者对机器学习文档的理解
Nicolas Frey1, Louis Agha-Mir-Salim1, Elena Hinz1
1Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Germany.
Studies in health technology and informatics
|May 17, 2025
概括
医疗保健专业人员对用于败血症预测模型的AI文档表现出适度的理解. 建议简化技术术语和使用视觉辅助工具,以改善理解和临床整合.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的人工智能
- 机器学习的可解释性
背景情况:
- 机器学习 (ML) 模型对于先进的临床决策支持系统至关重要.
- 目前的ML文档对医疗保健利益相关者构成技术障碍.
- 了解ML文档对于有效的临床整合至关重要.
研究的目的:
- 评估医疗保健利益相关者对ML模型文档的理解,用于毒症预测工具.
- 识别阻碍技术ML文档理解的障碍.
- 收集改善ML文档清晰度的建议.
主要方法:
- 与24名参与者进行了一次跨学科研讨会.
- 评估了参与者对败血症预测ML模型文档的理解.
- 测量了人工智能识字水平和理解百分比.
主要成果:
- 参与者表现出中度的人工智能素养 (平均得分:40.13/65).
- 平均而言,65%的ML模型文档被理解.
- 障碍包括技术术语,不明原因的缩写,缺乏上下文和密集的数据呈现.
结论:
- 技术文档阻碍了ML模型在临床工作流程中的可用性和集成.
- 简化语言,清晰的解释和视觉辅助对于增强理解至关重要.
- 提高文档清晰度将有助于在医疗保健中采用AI工具.
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