Analysis and Insights: Application of Large Language Models in Biomedical Academic Service Platforms

Corresponding author: Li Junlian, li.junlian@imicams.ac.cn

DOI: 10.12201/bmr.202604.00083

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  Abstract: Abstract Purpose/Significance Driven by the rapid evolution of LLMs, biomedical academic service platforms are undergoing a paradigm shift from information retrieval tools to research agents. This study systematically reviews the application characteristics of LLMs in this field and analyzes the differentiated evolution paths of domestic and international platforms, providing insights for constructing a trustworthy and intelligent academic service system. Method/Process A Technology–Domain–Scenario three-dimensional analytical framework was constructed. Ten representative domestic and international platforms (e.g., AMiner, Consensus, CNKI, and Wanfang) were selected as empirical samples. Attribution analysis was conducted across technical pathways, functional distributions, and resource ecosystems to reveal the structural differences in the intelligent evolution of various platforms. Result/Conclusion The study finds that functional variations among platforms result from the synergistic evolution of the three dimensions. International platforms predominantly follow a technology-driven and scenario-breakthrough path, focusing on semantic correlation and evidence generation within global academic resources. In contrast, domestic platforms exhibit significant local adaptation characteristics, integrating LLM technology with Chinese knowledge systems to deeply serve local research workflows and clinical practice needs. These differentiated pathways are shaped by language structures, terminological standards, and corpus conditions within their respective ecological backgrounds. Future developments should prioritize multimodal fusion, knowledge-augmented reliable generation, and the construction of compliant, closed-loop ecosystems to empower scientific innovation.

  Key words: Large Language Models (LLMs); Biomedicine; Academic Service Platforms; Deep Semantic Retrieval; Intelligent Interactive Question-Answering

  Submit time: 10 April 2026

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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