Article(id=1292126420210307174, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1292126377440989952, articleNumber=null, orderNo=null, doi=10.13699/j.cnki.1001-6821.2026.01.023, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1751299200000, receivedDateStr=2025-07-01, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1785996545125, onlineDateStr=2026-08-06, pubDate=1768579200000, pubDateStr=2026-01-17, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1785996545125, onlineIssueDateStr=2026-08-06, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1785996545125, creator=13701087609, updateTime=1785996545125, updator=13701087609, issue=Issue{id=1292126377440989952, tenantId=1146029695717560320, journalId=1246415772164075586, year='2026', volume='42', issue='1', pageStart='1', pageEnd='147', issueExtLink='null', onlineDate='null', pubDate='1768579200000', pubDateStr='2026-01-17', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1785996534929, creator='13701087609', updateTime=1786014708731, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1292202603945947673, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1292126377440989952, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1292202603950141978, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1292126377440989952, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=142, endPage=147, ext={EN=ArticleExt(id=1292126420428410983, articleId=1292126420210307174, tenantId=1146029695717560320, journalId=1246415772164075586, language=EN, title=Introduction to the guiding principles on the use of large language models in regulatory science and for medicines regulatory activities issued by the European Medicines Agency, columnId=1291339025546211579, journalTitle=Chinese Journal of Clinical Pharmacology, columnName=Drug Evaluation and Administration, runingTitle=null, highlight=null, articleAbstract=

With the advancement of artificial intelligence, the European Medicines Agency released " Guiding principles on the use of large language models in regulatory science and for medicines regulatory activities" in 2024, emphasizing the importance of the safe and responsible use of large language models. This guidance covers general ethical considerations, user principles and organizational principles, clearly outlining the potential of large language models in areas such as text processing assistance and data mining, while also warning of risks such as hallucinations, data privacy issues, and biased outputs. It proposes measures including continuous learning, risk monitoring and mechanism-building to address these challenges. Although China has not yet issued similar guidelines, large language models hold potential application prospects in areas such as assisting in the processing of review materials, formulating and revising guidance principles and identifying risks. At the same time, challenges related to decision interpretability, data bias, legal accountability and talent reserves remain. It is recommended that China’s regulatory authorities draw on international experience to construct an application framework tailored to national conditions as soon as possible, thereby promoting the integration of artificial intelligence technology with the field of drug regulation.

, authors=Jian-hong PAN^{1, 2}, Shang CAO³, Jun ZHAO^{1, 2}, authorsList=Jian-hong PAN, Shang CAO, Jun ZHAO, authorCompany=null, correspAuthors=Jian-hong PAN, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, fund=null), CN=ArticleExt(id=1292126421560873064, articleId=1292126420210307174, tenantId=1146029695717560320, journalId=1246415772164075586, language=CN, title=关于欧洲药品管理局发布的监管科学和药品监管工作中大语言模型的使用指导原则的介绍, columnId=1291339025730760957, journalTitle=中国临床药理学杂志, columnName=药物评价与管理, runingTitle=null, highlight=null, articleAbstract=

随着人工智能发展，欧洲药品管理局于2024年发布《监管科学和药品监管工作中大语言模型的使用指导原则》，强调安全、合规、有效使用大语言模型的重要性。该指导原则涵盖一般伦理考量、用户原则及组织管理机制，明确指出大语言模型在辅助文本处理、数据挖掘等方面的潜力，同时警示其在幻觉、数据隐私、偏见输出等方面的风险，提出需通过持续学习、风险监控和机制建设等予以应对。我国虽尚未发布类似指南，但大语言模型在审评资料辅助处理、指导原则制修订、风险识别等方面具备潜在的应用前景，同时还面临决策可解释性、数据偏见、法律问责、人才储备等挑战。建议我国监管机构参考国际经验，尽早构建符合国情的应用框架，推动人工智能技术与药品监管领域的融合。

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潘建红（1986-），男，副研究员，主要从事药品的技术审评工作

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