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与生成性AI共同创建自动化mHealth应用程序系统审查过程:设计科学研究方法

Guido Giunti^1,2,3, Colin P Doherty^1,3,4

¹Academic Unit of Neurology, School of Medicine, Trinity College Dublin, Dublin, Ireland.

JMIR medical education

|February 12, 2024

概括

生成型人工智能可以自动化移动健康 (mHealth) 应用程序的系统审查,节省时间和资源. 这种方法有助于研究人员,但方法和数据质量存在局限性.

科学领域:

医疗信息学医疗信息学
研究中的人工智能.

背景情况:

移动健康 (mHealth) 服务正在迅速扩大,增加了对系统审查的需求.
系统审查是必不可少的,但耗时且资源密集.
生成型人工智能为自动化这些审查任务提供了潜在的解决方案.

研究的目的:

调查使用生成AI用于自动化mHealth中的系统审查任务的可行性.
在这种情况下,评估生成AI的能力和局限性.

主要方法:

使用了设计科学研究方法.
与生成性AI (ChatGPT) 共同创作,开发用于系统审查的自动化软件代码.
利用通过对话性AI生成的Python脚本从Google Play商店提取和分析mHealth应用数据.

主要成果:

一个Python脚本成功地开发并使用生成AI协助进行调试.
该脚本自动识别mHealth解决方案,并在应用程序描述中进行关键字搜索.
结果被出口并与现有的系统审查成果进行比较,证明了自动化潜力.

结论:

生成型人工智能显示了自动化mHealth应用程序系统审查的巨大潜力.
这种方法可以使具有有限编码专业知识的研究人员受益.
限制包括潜在的偏见和对培训数据质量的依赖.

关键词:

在这里,我们可以看到AIAIAI.人工智能工具是AI工具.聊天GPT 聊天在GPT 聊天应用程序应用程序应用程序设计科学研究设计科学研究电子健康电子健康有证据的证据库.生成型的人工智能生成型的人工智能 (GAI)语言模型语言模型移动健康移动健康移动健康移动健康 m健康干预措施 m健康干预措施移动设备是移动设备.定性研究是一种定性研究.软件代码是一个软件代码.系统性审查系统性审查

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