AI-Powered Affiliation Insights: LLM-Based Bibliometric Study of European Medical Informatics Conferences.

Journal: Studies in health technology and informatics
Published Date:

Abstract

This study employs Large Language Models (LLMs) to analyze bibliometric data from European Medical Informatics conferences from 1996 to 2024. By enhancing traditional methods with LLM-based techniques, the researchers significantly improved affiliation extraction accuracy. The analysis reveals trends in publication volume, author impact, and institutional collaborations across Europe. Key findings include the identification of leading contributors, visualization of collaboration networks, and mapping of geographical and institutional centers of excellence. The study highlights the potential of LLMs in bibliometric analysis, offering deeper insights into research trends and collaborations while addressing challenges in data standardization and computational resources.

Authors

  • Ehsan Bitaraf
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Maryam Jafarpour
    Department of Algorithms and Computation, School of Engineering Science, College of Engineering, University of Tehran, Tehran, Iran.