使用大型语言模型来识别伪信息:对健康信息的影响
1Department of Rehabilitation Sciences, Faculty of Health, University of Witten/Herdecke, Witten, Germany.
Health information and libraries journal
|March 20, 2025
概括
大型语言模型 (LMM) 在检测健康错误信息方面表现出不同的能力. 虽然一些模型最初发现了不准确性,但结果不一致,突出了需要提高AI准确性,以获得可靠的健康信息.
科学领域:
- 人工智能在健康信息中的应用
- 用于科学文献的自然语言处理.
背景情况:
- 开放式的研究对健康自我教育至关重要.
- 由人工智能驱动的大型语言模型 (LMM) 显示出识别健康错误信息的潜力.
研究的目的:
- 评估LMMs在检测健康产品中的伪信息方面的有效性.
- 评估领先的LMM能够识别错误的健康内容的能力.
主要方法:
- 测试了四个LMM应用程序 (ChatGPT-4o,Claude 3.5 Sonnet,Gemini,Copilot) 进行了测试.
- 评估LMMs在开放获取健康文章中识别不准确性的能力.
主要成果:
- 最初,ChatGPT-4o和Claude发现了大多数不准确;双子座和副驾驶员表现不佳.
- 聊天GPT-4o的准确性是不可重现的;克劳德始终检测到关键问题.
- 双子飞机和副驾驶飞机在验证期间的性能变化很小.
结论:
- 在LMM检测健康伪信息的能力方面存在显著差异.
- 不一致的LMM输出对可靠的应用构成了挑战.
- 需要进一步提高准确性,以便患者和卫生专业人员可以安全使用LMM.
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