生成性人工智能和健康错误信息:生产,传播和缓解 - 一个系统的审查
Hamid Reza Saeidnia1, Shamim Jahani2, Nasrin Ghiasi3
1Department of Knowledge and Information Science, Tarbiat Modares University, (TMU), Tehran, Iran.
BMC public health
|January 28, 2026
概括
生成型人工智能 (AI) 通过增加生产量和可信度,使健康错误信息升级. 目前的检测和缓解策略对人工智能产生的内容的有效性有限.
科学领域:
- 公共卫生 公共卫生
- 人工智能的人工智能
- 信息科学 信息科学 信息科学
背景情况:
- 生成型人工智能破坏信息生态系统,通过可扩展,令人信服的虚假健康叙述创造新的公共卫生威胁.
- 这篇评论探讨了生成性AI如何重塑健康错误信息的创建,传播和适度.
研究的目的:
- 系统地审查和综合有关生成人工智能对健康错误信息影响的证据.
- 分析健康错误信息创建,传播和调节过程的重新配置.
主要方法:
- 按照PRISMA 2020指南进行系统审查.
- 包括2023年1月至2025年8月期间发表的15项经验研究.
- 咨询了医疗,计算机科学和社会科学领域的主要科学数据库.
主要成果:
- 生成型人工智能显著提高了健康虚假信息的数量,速度和可信度.
- 用户难以区分人工智能产生的错误信息和人类撰写的错误信息;分享不仅仅是以准确性为导向的.
- 检测系统的有效性有限,标签干预措施对感知准确性表现出上下文依赖的影响.
结论:
- 生成型人工智能降低了创造障碍,并利用平台/行为动态,改变了健康错误信息格局.
- 目前的缓解策略 (技术,社会技术,治理) 是有希望的,但是在芽和不均的评估.
- 未来的研究需要专注于多式联运,多语言,健康特定验证和现实世界的干预测试,以实现弹性健康信息生态系统.
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