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

Year: 2025

Type: book-chapter

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.

Source: Studies in health technology and informatics

DOI: 10.3233/shti250474

Authors: Ehsan Bitaraf, Maryam Jafarpour

Institutions: Iran University of Medical Sciences, Medical University of Vienna

Language: English

Topic: scientometrics and bibliometrics research

Subfield: Statistics, Probability and Uncertainty

Field: Decision Sciences

Domain: Social Sciences

Open Access status: hybrid

OpenAlex ID: W4410448263

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