构建生物医学期刊的AI影响力数据集，分析合作与AI对期刊声誉的影响。
BioMedJImpact: A Comprehensive Dataset and LLM Pipeline for AI Engagement and Scientific Impact Analysis of Biomedical Journals
- 用三阶段LLM pipeline提取期刊每年的AI相关文章占比。
- 发现大团队合作提升引用影响力，但仅2019年AI占比与影响因子正相关。
- 数据集覆盖174万文献，适合研究医学期刊演化与AI融合趋势者使用。
评估期刊影响力是学术传播的核心，但现有资源很少捕捉合作与人工智能（AI）研究如何共同塑造生物医学领域期刊声望。我们提出BioMedJImpact，一个基于2744个期刊、涵盖174万篇PubMed Central文章的大规模生物医学数据集。该数据集整合了引文指标、合作特征及通过可复现的三阶段LLM流程计算的AI参与率（即每本期刊-年份中与AI相关的文章比例）。我们分析了2016–2019和2020–2023两个时间段内，合作强度与AI参与率对科学影响力的联合影响。结果显示：作者团队越大，期刊引用影响力越高；而仅在2019年，AI参与率与影响因子呈正相关。通过人工评估验证了LLM管道在检测AI相关性上的高一致性，并保持子领域分类的一致性。BioMedJImpact不仅提供生物医学与AI交汇处的综合性数据，还建立了一个可扩展、内容感知的计量分析框架。代码与数据集可在https://github.com/JonathanWry/BioMedJImpact 获取。
原文摘要 · Abstract (English)
Assessing journal impact is central to scholarly communication, yet existing resources rarely capture how collaboration and artificial intelligence (AI) research jointly shape venue prestige in biomedicine. We present BioMedJImpact, a large-scale, biomedical-oriented dataset built from 1.74 million PubMed Central articles across 2,744 journals. BioMedJImpact integrates bibliometric indicators, collaboration features, and an LLM-derived AI engagement rate, defined as the proportion of AI-related articles within each journal-year. Specifically, AI engagement rate is extracted through a reproducible three-stage LLM pipeline. We analyze how collaboration intensity and AI engagement rate jointly influence scientific impact across two temporal subsets (2016-2019, 2020-2023). Two main patterns emerge: journals with larger author teams tend to have higher citation impact, while AI engagement rate is positively associated with Impact Factor only in the 2019 subset. To validate the LLM pipeline for deriving the AI engagement rate, we conduct human evaluation, confirming substantial agreement in AI relevance detection and consistent subfield classification. Together, BioMedJImpact provides both a comprehensive dataset at the interface of biomedicine and AI and a validated framework for scalable, content-aware scientometric analysis. Code and dataset are available at https://github.com/JonathanWry/BioMedJImpact.
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