医学文献分析的大型语言模型的潜力和局限性:初步调查
Takahiro Kamihara1, Takuya Omura2, Atsuya Shimizu1
1Department of Cardiology, National Center for Geriatrics and Gerontology, Obu, JPN.
Cureus
|October 20, 2025
概括
大型语言模型 (LLM) 可以提取关键词并识别医学文献的趋势,为传统文本挖掘提供一种补充方法. 然而,它们的黑盒性质和主观解释存在局限性.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 图书统计学 图书统计学
背景情况:
- 大型语言模型 (LLM) 在医疗应用中具有潜力,但在透明度和可重复性方面面临挑战.
- 传统的文本挖掘已经建立,但需要大量的时间投资.
研究的目的:
- 评估一个LLM是否可以实现与传统文本挖掘相比较的文献分析结果.
- 在医学文本分析中确定LLM的实用性和局限性.
主要方法:
- 分析了5112个PubMed摘要,使用关键词"文本挖掘".
- 使用Google Gemini 2.5进行概念和趋势提取.
- 与VOSviewer和KH Coder输出的定性比较.
主要成果:
- 谷歌双子在概念上汇总了单词,并确定了研究趋势.
- 由LLM生成的共发生网络概念与传统工具的视觉相似性.
- 在LLM中,结果显示了独特的解释,限制了直接的统计比较.
结论:
- 法律法规可以提取关键词,概念和趋势,具有概念总结和趋势检测的潜力.
- 限制包括黑子的性质,可复制性问题和主观解释.
- 当理解约束时,LLM可以成为加快医学文献分析的宝贵补充工具.
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