Ctrl + Alt + Conceive:人工智能时代的生育意识,大型语言模型如何进行比较?
Bola Grace1,2,3, Jiarui Zhu2, Hema Dudakia1
1UCL Institute for Women's Health, Faculty of Population Health Sciences, University College London, London, UK.
Human fertility (Cambridge, England)
|November 17, 2025
概括
大型语言模型 (LLM) 提供可访问的生育信息,但准确性各不相同. 虽然在基本主题上通常可靠,但LLM在辅助生殖技术等复杂领域扎,需要仔细的质量评估.
科学领域:
- 生殖健康 生殖健康
- 医疗保健中的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 在医疗保健中越来越多地用于信息传播.
- 人工智能日益普及,需要对LLMs提供的健康信息质量进行评估.
研究的目的:
- 质量评估由领先的LLM平台生成的生育信息的准确性和可理解性.
- 为了比较ChatGPT,Gemini,Copilot和Perplexity在提供生育相关健康信息方面的表现.
主要方法:
- 分析了五个生育主题 (月经周期,受孕,风险因素,ART,与年龄相关的衰退) 的37个提示.
- 由LLM生成的内容被评估为一致性,可理解性和简洁性.
- 对于每个平台,记录了安全警告和免责声明.
主要成果:
- 在基本的生育主题上,LLM提供了普遍一致的信息,但在辅助生殖技术中显示了不准确的信息.
- 困惑表现出最广泛的响应对应范围 (高和低).
- 聊天GPT提供了最简洁的答案,在平台上具有相似的可理解性;并非所有平台都发布了安全警告.
结论:
- 法律法规提供了一个重要的机会,为各种利益相关者扩大获得生育和生殖健康信息的机会.
- 确保LLM产生的内容的准确性和质量对于可靠的健康指导至关重要.
- 需要仔细关注LLM输出质量,以支持生育和生殖健康方面的知情决策.
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