Article(id=1291707075625767367, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1291706966187991876, articleNumber=null, orderNo=null, doi=10.13699/j.cnki.1001-6821.2025.24.019, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1753804800000, receivedDateStr=2025-07-30, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1785896565585, onlineDateStr=2026-08-05, pubDate=1766851200000, pubDateStr=2025-12-28, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1785896565585, onlineIssueDateStr=2026-08-05, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1785896565585, creator=13701087609, updateTime=1785896565585, updator=13701087609, issue=Issue{id=1291706966187991876, tenantId=1146029695717560320, journalId=1246415772164075586, year='2025', volume='41', issue='24', pageStart='3451', pageEnd='3600', issueExtLink='null', onlineDate='null', pubDate='1766851200000', pubDateStr='2025-12-28', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1785896539494, creator='13701087609', updateTime=1785998798662, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1292135872292479273, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1291706966187991876, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1292135872296673578, tenantId=1146029695717560320, journalId=1246415772164075586, issueId=1291706966187991876, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=3567, endPage=3574, ext={EN=ArticleExt(id=1291707078029103560, articleId=1291707075625767367, tenantId=1146029695717560320, journalId=1246415772164075586, language=EN, title=Advances in artificial intelligence for drug safety, columnId=1246531408630534361, journalTitle=Chinese Journal of Clinical Pharmacology, columnName=Review, runingTitle=null, highlight=null, articleAbstract=
Drug safety spans the full lifecycle of drug research and development, manufacture, distribution and usage, serving as both the cornerstone of public health maintenance and a core issue in regulatory science. Traditional drug safety monitoring models face numerous challenges regarding data processing efficiency, agility in risk identification and comprehensiveness. However, the advancement of artificial intelligence (AI) technologies offers unprecedented opportunities for this field. From a full lifecycle perspective, this paper systematically reviews and analyzes the key technologies and application progress of AI in safety assessment across three stages: preclinical research, clinical trials, and post-marketing pharmacovigilance. Currently, AI technologies, represented by machine learning and natural language processing, have demonstrated significant potential in drug toxicity prediction, the automated identification of adverse events, and real-time safety monitoring. Nevertheless, uneven data quality, insufficient model interpretability, and imperfect regulatory compliance remain the primary bottlenecks hindering the widespread application of AI in the field of drug safety. Future development will focus on the fusion of multi-modal data, the enhancement of model interpretability, and the application of federated learning for privacy protection, with the aim of constructing a more precise and reliable intelligent drug safety monitoring system.
, authors=Xu CHEN
1, 2, Li-xia FU
1, 2, Yi-min CUI
1, 2, 3, authorsList=Xu CHEN, Li-xia FU, Yi-min CUI, authorCompany=null, correspAuthors=Li-xia FU, Yi-min CUI, 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=1291707078935073226, articleId=1291707075625767367, tenantId=1146029695717560320, journalId=1246415772164075586, language=CN, title=人工智能技术在药品安全领域的应用进展, columnId=1246531408819278046, journalTitle=中国临床药理学杂志, columnName=综述, runingTitle=null, highlight=null, articleAbstract=
药品安全贯穿药物研发、生产、流通及使用的全生命周期,既是维护公众健康的基石,也是监管科学领域的核心议题。传统药品安全监测模式在数据处理效率、风险识别敏捷性和监测全面性等方面存在诸多挑战,人工智能(AI)技术的发展为该领域带来了前所未有的机遇。本文从全生命周期视角出发,系统回顾和分析了AI在药品临床前研究、临床试验阶段及上市后药物警戒3个阶段中,用于安全评估的关键技术与应用进展。目前,以机器学习、自然语言处理等为代表的AI技术,已在药品毒性预测、不良事件自动识别和实时安全监测等方面展现出巨大应用潜能。然而,数据质量不均衡、模型可解释性不足以及监管合规性尚不完善,仍是AI技术广泛应用于药品安全领域的主要瓶颈。未来,融合多模态数据、提升模型解释性以及强化隐私保护的联邦学习技术将成为重点发展方向,以期构建更精准可靠的药品安全智能监测体系。
, authors=陈旭
1, 2, 傅丽霞
1, 2, 崔一民
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陈旭(2000-),男,博士研究生,主要从事药品监管科学研究
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