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New dataset and LLM pipeline analyze AI's impact on biomedical journals

By PulseAugur Editorial · [1 sources] ·

Researchers have developed BioMedJImpact, a new dataset and pipeline designed to analyze the impact of artificial intelligence (AI) on biomedical journals. The dataset, derived from over 1.74 million articles across 2,744 journals, incorporates bibliometric data, collaboration metrics, and an AI engagement rate calculated using a three-stage LLM pipeline. Initial analysis indicates that larger author teams correlate with higher citation impact, while AI engagement shows a positive association with Impact Factor only in a specific recent subset of data. AI

IMPACT Provides a framework for understanding AI's evolving role and influence within scientific research communities.

RANK_REASON The item describes a new dataset and methodology for analyzing scientific impact, presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New dataset and LLM pipeline analyze AI's impact on biomedical journals

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The item describes a new dataset and methodology for analyzing scientific impact, presented in an arXiv paper. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Jiaying Lu ·

    BioMedJImpact: A Comprehensive Dataset and LLM Pipeline for AI Engagement and Scientific Impact Analysis of Biomedical Journals

    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 buil…

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