通讯作者: 李军莲, li.junlian@imicams.ac.cn

DOI：10.12201/bmr.202604.00083

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

• 摘要：摘要 目的/意义 随着LLMs的快速演进，生物医学学术服务平台正经历从“信息定位工具”向“研究智能体”的范式转型。本文旨在系统梳理LLMs在该领域的应用特征，分析国内外平台的差异化演进路径，为构建可信、智能的学术服务体系提供启示。 方法/过程 构建“技术—领域—场景”三维框架，遴选10个典型国内外平台（如AMiner、Consensus、CNKI、万方），从技术路径、功能分布及资源生态等维度归因分析，揭示平台智能化演进的结构性差异。 结果/结论 研究发现，平台功能的差异本质上是三维要素协同演化的结果：国际平台多遵循“技术—领域—场景”路径，侧重全球学术资源的语义关联与证据生成；国内平台则表现出显著的“本土化适配”特征，通过融合大模型技术与中文知识体系，深耕本土科研工作流与临床实践需求。这种差异化路径由各生态背景下的语言结构、术语规范及语料条件共同塑造。未来应加强多模态融合、知识增强的可靠生成路径及合规闭环生态建设，以赋能科研创新。

  关键词： 大语言模型; 生物医学; 学术服务平台; 深度语义检索; 智能交互问答

   

  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

  提交时间：2026-04-10

  版权声明：作者本人独立拥有该论文的版权，预印本系统仅拥有论文的永久保存权利。任何人未经允许不得重复使用。
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• 序号	提交日期	编号	操作
  1	2025-12-16	10.12201/bmr.202604.00083V1	下载

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