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中国医药导刊 ›› 2025, Vol. 27 ›› Issue (1): 1-6.

• 监管科学 •    下一篇

药品监管领域大语言模型(LLM)一体化建设策略研究

陈锋, 吴欣然   

  1. 国家药品监督管理局信息中心,北京 100076
  • 收稿日期:2025-03-11 出版日期:2025-01-28 发布日期:2025-01-28

Research on the Integrated Construction Strategy of Large Language Models LLM in Drug Regulation

  1. Center for Information of NMPA Beijing 100076, China
  • Received:2025-03-11 Online:2025-01-28 Published:2025-01-28

摘要:

大语言模型(LLM)技术是近年来人工智能(AI)领域最为重要的突破之一,核心在于利用大规模数据训练和深度学习(DL)算法构建具备强大泛化能力的模型,应用场景极为广泛,涵盖自然语言处理、计算机视觉、语音识别等多个领域。目前,LLM技术在AI领域取得显著进展,为药品监管智能化升级带来新的机遇。本研究在梳理全球药品监管机构积极探索药品监管领域LLM应用现状的基础上,深入研究了药品监管领域LLM一体化建设策略,分析了当前应用现状及存在问题,如统筹管理欠缺、算力资源短缺、数据安全与质量问题、模型质量不足等。针对这些问题,提出了一体化建设思路,包括统一部署基础LLM、结合药品监管数据精调主领域LLM、规划布局与构建子领域LLM、基于领域模型蒸馏轻量应用模型、建立模型集中训练与共享机制等顶层设计思路。同时,明确了国家药品监督管理局和省级药品监管部门在一体化建设中的推进重点,旨在通过强化顶层设计和协同合作,实现技术互通、生态整合,推动AI技术在药品监管领域的广泛应用,提升监管效能,保障公众健康和药品安全。


关键词:  , 大语言模型(LLM);药品监管;一体化建设;人工智能(AI);智能化监管

Abstract:

Large language model LLM technology is one of the most important breakthroughs in the field of artificial intelligence AI in recent years. LLM core technology is to use large-scale data training and deep learning DL algorithms to construct models with strong generalization ability and the application scenarios are extremely extensive covering language processing computer vision speech recognition and other fields. In recent years LLM technology has made significant progress in the field of AI which bringing new opportunities for the intelligent upgrade of drug regulation. Based on the current situation of the active exploration of the application of LLM in the field of drug regulation by global drug regulatory agenciesthis study investigates the integrated construction strategies of LLM in the field of drug regulation analyzes the current application status and existing problems such as lack of coordination insufficient computing power data security and quality issues and inadequate model quality. To address these issues an integrated construction approach is proposed including unified deployment of basic LLM fine-tuning of main domain LLM with drug regulation data planning and constructing of sub-domain LLM distilling lightweight application models based on domain models and establishing centralized training and sharing mechanisms for models. It also clarifies the key tasks of national and provincial drug regulatory authorities in integrated construction aiming to strengthen top-level design and collaborative efforts to achieve technical interoperability and ecosystem integration promote the widespread application of AI in the field of drug regulation enhance regulatory efficiency and ensure public health and drug safety.


Key words:  , Large language model , LLM); , Drug regulation , Integrated construction , Artificial intelligence , AI); , Intelligent regulation

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