人工智能在社交媒体上预防物质使用方面的创新:范围审查
Van Thanh Nguyen1, Huy Phan Khanh Le2, Giang Minh Le3
1School of Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Viet Nam.
Public health
|March 8, 2026
概括
人工智能 (AI) 和机器学习 (ML) 在社交媒体上显示出对物质使用预防的希望. 然而,数据不平衡和道德问题等挑战需要解决,以实现有效实施.
科学领域:
- 公共卫生 公共卫生
- 计算机科学 计算机科学
- 数字健康数字健康
背景情况:
- 社交媒体平台越来越多地被用于公共卫生干预.
- 人工智能 (AI) 和机器学习 (ML) 为预防物质使用提供了新的方法.
- 了解当前AI/ML应用在这个领域的现状至关重要.
研究的目的:
- 对应用在社交媒体上的AI/ML模型进行范围审查,以预防物质使用.
- 确定关键挑战,并为实施这些技术提供建议.
主要方法:
- 在七个数据库中进行了全面的文献搜索,截至2024年11月.
- 该审查遵循了阿克西和奥马利框架和PRISMA-ScR指南.
- 数据进行了叙事合成,重点关注研究特征,AI/ML模型和结果.
主要成果:
- 包括15项研究,主要使用AI/ML进行趋势监测,风险预测和推特等平台上的竞选评估.
- 自然语言处理是应用最常见的AI/ML技术.
- 发现的关键挑战包括语言模两可,模型可解释性,数据不平衡以及道德考虑.
结论:
- 目前在社交媒体中用于预防物质使用的AI / ML应用程序通常是描述性的,缺乏实时参与.
- 局限性包括报告不足,样本特征不清楚以及不一致的措施.
- 未来的研究应该集中在可解释的人工智能,人类验证,跨平台集成和适应本地干预措施上.
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