探索人工智能工具产生的研究优先事项的变化
John Garry1, Mark Tomlinson2,3, Maria Lohan3,4
1Queen's University Belfast, Department of Politics and International Relations, Northern Ireland, UK.
Journal of global health
|January 30, 2026
概括
大型语言模型 (LLM) 可以帮助制定卫生研究优先级. 双子座的输出与其他人工智能工具保持一致,而DeepSeek和Perplexity分别提供了不同的技术和公共卫生视角.
科学领域:
- 卫生研究 卫生研究
- 人工智能的人工智能是人工智能.
- 医疗信息学医学信息学
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于研究.
- 人工智能在与健康相关的研究重点设定练习 (RPSEs) 中的作用正在出现.
- 不同的人工智能工具产生的研究优先级差异尚未得到充分理解.
研究的目的:
- 对四种疾病 (癌症,COVID-19,艾滋病毒,阿尔茨海默氏症) 的五种AI工具产生的研究优先级进行比较.
- 用相同和修改的提示来评估AI工具输出之间的差异稳定性.
主要方法:
- 使用SBERT-BioBERT嵌入和共弦相似度对AI工具输出进行比较分析.
- 评估了五种人工智能工具:DeepSeek,ChatGPT,克劳德,困惑和双子座.
- 通过快速重新运行和修改进行稳定性评估.
主要成果:
- 双子座的输出与其他AI工具非常相似.
- 深度搜索和困惑产生了最明显的输出.
- DeepSeek专注于技术医学问题,而Perplexity强调公共卫生问题,这是一个稳定的区别.
结论:
- 双子座适合研究人员更喜欢单一的人工智能模型的健康研究优先事项.
- 当使用多个AI模型时,DeepSeek和Perplexity提供了互补的见解.
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