Deciphering Scientific Collaboration in Biomedical LLM Research: Dynamics, Institutional Participation, and Resource Disparities

2025-11-02

AI Summary

This study investigates how large language models (LLMs) reshape structural equity in biomedical research collaboration. Methodologically, we construct a temporal co-authorship network from 5,674 LLM-related publications indexed in PubMed (1990–2023), integrating bibliometric analysis, centrality metrics, and interdisciplinary pattern mining. Our results reveal: (1) interdisciplinary collaboration has intensified, yet computer science participation has declined—highlighting medicine and computational disciplines as critical bridging fields; (2) elite institutions (e.g., Stanford, Harvard) consistently occupy central network positions, with resource access intensity strongly positively correlated with publication impact; (3) beneath the surface of “technological democratization,” a resource-driven hierarchical structure persists, and we identify compensatory collaboration mechanisms adopted by resource-constrained yet high-output institutions. These findings provide empirical grounding and a methodological framework for assessing research equity in the AI era.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Social Networks and Social Media: Computational social science
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Large language models (LLMs) are increasingly transforming biomedical discovery and clinical innovation, yet their impact extends far beyond algorithmic revolution-LLMs are restructuring how scientific collaboration occurs, who participates, and how resources shape innovation. Despite this profound transformation, how this rapid technological shift is reshaping the structure and equity of scientific collaboration in biomedical LLM research remains largely unknown. By analyzing 5,674 LLM-related biomedical publications from PubMed, we examine how collaboration diversity evolves over time, identify institutions and disciplines that anchor and bridge collaboration networks, and assess how resource disparities underpin research performance. We find that collaboration diversity has grown steadily, with a decreasing share of Computer Science and Artificial Intelligence authors, suggesting that LLMs are lowering technical barriers for biomedical investigators. Network analysis reveals central institutions, including Stanford University and Harvard Medical School, and bridging disciplines such as Medicine and Computer Science that anchor collaborations in this field. Furthermore, biomedical research resources are strongly linked to research performance, with high-performing resource-constrained institutions exhibiting larger collaboration volume with the top 1% most connected institutions in the network. Together, these findings reveal a complex landscape, where democratizing trends coexist with a persistent, resource-driven hierarchy, highlighting the critical role of strategic collaboration in this evolving field.

Problem

• Analyzing how LLMs reshape scientific collaboration structure in biomedical research
• Identifying institutional participation and bridging disciplines in LLM biomedical networks
• Assessing how resource disparities influence research performance in biomedical LLMs

Innovation

• Analyzed 5,674 biomedical LLM publications from PubMed
• Examined collaboration diversity and network central institutions
• Assessed resource disparities and strategic collaboration impacts