弥合差距:走向评估移动健康和人工智能支持的健康应用的标准化框架
Sahrish Mahmood Syed1, Aby Mathews Maluvelil2,3, Matvey Kipershtein3
1George Brown College, Toronto, Canada.
Studies in health technology and informatics
|February 13, 2026
概括
评估移动健康 (mHealth) 和人工智能健康应用是不一致的. 本次审查确定了关键的评估属性,并提出了一个全面的分类法,以提供更好的指导.
科学领域:
- 数字健康数字健康
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 移动健康 (mHealth) 和支持人工智能的健康应用正在迅速发展.
- 目前对这些技术的评估框架是分散的,缺乏普遍采用.
- 这种不一致性阻碍了患者,临床医生和政策制定者的明确指导.
研究的目的:
- 在评估移动健康和人工智能健康应用中,系统地审查和识别常被引用和被忽视的属性.
- 对人工智能治理和评估指南进行专题分析.
- 将发现综合成一个综合的,多领域的分类学,以进行全面的评估.
主要方法:
- 对2014年至2024年间发表的研究进行系统文献综述.
- 识别和分析经常提及的评估属性 (例如可用性,参与度,隐私).
- 人工智能治理和评估指南文件的主题分析.
主要成果:
- 通常识别的属性包括可用性,参与度和隐私.
- 还强调了被忽视的属性,如成本和用户保留.
- 开发了一个多领域的分类法,涵盖战略目的,风险,安全,公平,透明度,可解释性和技术生命周期.
结论:
- 需要一个整体的基础来评估mHealth和AI健康应用.
- 拟议的分类学为一致和彻底的评估提供了一个全面的框架.
- 这项工作旨在改善数字健康领域的利益相关者指导.
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