Article(id=1321425581430915819, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425569611362746, articleNumber=null, orderNo=null, doi=10.20251/j.cnki.1003-3734.2026.07.008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=null, receivedDateStr=null, revisedDate=null, revisedDateStr=null, acceptedDate=1755273600000, acceptedDateStr=2025-08-16, onlineDate=1792982009513, onlineDateStr=2026-10-26, pubDate=1776182400000, pubDateStr=2026-04-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792982009513, onlineIssueDateStr=2026-10-26, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792982009513, creator=13701087609, updateTime=1792982009513, updator=13701087609, issue=Issue{id=1321425569611362746, tenantId=1146029695717560320, journalId=1235980733773295621, year='2026', volume='35', issue='7', pageStart='673', pageEnd='784', issueExtLink='null', onlineDate='null', pubDate='1776182400000', pubDateStr='2026-04-15', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792982006695, creator='13701087609', updateTime=1792983136833, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1321430309837587328, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425569611362746, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1321430309837587329, tenantId=1146029695717560320, journalId=1235980733773295621, issueId=1321425569611362746, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=727, endPage=733, ext={EN=ArticleExt(id=1321425583117026028, articleId=1321425581430915819, tenantId=1146029695717560320, journalId=1235980733773295621, language=EN, title=Bibliometric analysis of AI technologies applied in drug clinical trials: perspectives from global and Chinese research domains, columnId=null, journalTitle=Chinese Journal of New Drugs, columnName=null, runingTitle=null, highlight=null, articleAbstract=
Objective: To analyze the current status, hotspots, and trends of artificial intelligence (AI) technologies applied in the field of drug clinical trials, providing a reference for the application of AI technologies in this domain.
Methods: A visual analysis of literature on AI applications in drug clinical trials was conducted using CiteSpace across Chinese databases (CNKI and Wanfang) and the English database (Web of Science).
Results: A total of 135 Chinese and 122 English publications were included. The volume of Chinese publications on AI technologies in drug clinical trials exhibited fluctuating growth, while international English publications demonstrated steady and rapid growth. The institution with the highest number of domestic publications was the China National Medical Products Administration, while internationally, it was Harvard University. Collaborative networks among researchers were relatively decentralized both domestically and internationally. Domestic collaborations primarily occurred among pharmaceutical companies, exhibiting a fragmented overall structure. International collaborations were concentrated among universities, with regional clusters of intensive collaboration. Keyword co-occurrence and clustering analyses revealed both commonalities and differences in AI applications between domestic and international reaserch. Currently, machine learning technologies represent a hotspot in both domestic and international research. Applications in participant recruitment are observed globally, and machine learning is leveraged for data analysis to optimize clinical trials, assess outcomes, and diagnose diseases. Domestically, AI applications focused on clinical trial data management, information system design, and implementation. Internationally, key areas included patient recruitment, disease diagnosis within trials, and biomarker prediction. Domestic research emphasized compliance in AI applications, whereas international research prioritized the refinement of AI algorithmics.
Conclusion: The application of AI in drug clinical trials is evolving from foundational data processing toward complex scenario implementation. Future research may advance in areas such as standardization of data formats, data compliance, and the application of AI in complex clinical trial scenarios.
, authors=Xue-mei XIONG
1, 2, Jin GU
2, Chen-xi HU
2, Yuan-kai HUANG
1, Hou-ming XU
2, *, authorsList=Xue-mei XIONG, Jin GU, Chen-xi HU, Yuan-kai HUANG, Hou-ming XU, authorCompany=null, correspAuthors=Hou-ming XU, 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=1321425583339324141, articleId=1321425581430915819, tenantId=1146029695717560320, journalId=1235980733773295621, language=CN, title=人工智能技术在国内外药物临床试验领域中应用的文献计量学分析, columnId=1236331741447778310, journalTitle=中国新药杂志, columnName=药物临床试验规范与进展专栏, runingTitle=null, highlight=null, articleAbstract=
目的： 对药物临床试验领域应用的人工智能（artificial intelligence, AI）技术的现状、热点和趋势进行分析，为AI技术在药物临床试验中的应用提供参考。
方法： 使用CiteSpace在中文数据库知网、万方，英文数据库Web of Science中对AI在药物临床试验领域应用情况的文献进行可视化分析。
结果： 共纳入中文文献135篇，英文文献122篇。我国AI技术在药物临床试验领域发文量呈波动增长，国外发文量呈稳定增长且增长速度较快。国内文献发表数量最多的科研机构为国家药品监督管理局，国外为Harvard University。国内外研究者之间合作关系较为分散。国内合作机构主要出现在医药公司间，整体合作结构分散。国外合作机构出现在各高校之间，存在局部密集合作群组。根据关键词共现与聚类分析，国内外AI技术的应用情况存在共性，AI技术的应用领域存在差异。目前机器学习技术均为国内外研究的热点，国内外在受试者招募上都有应用，机器学习可应用于数据分析以实现临床试验的优化、结果评估、疾病诊断等。国内AI技术在临床试验中的应用领域包括临床试验数据管理、信息系统设计和应用，国外应用领域主要包括患者招募、临床试验中的疾病诊断、生物标志物预测等。国内更注重AI技术应用的合规性，国外注重AI技术算法的深耕。
结论： AI在药物临床试验领域的应用正从基础数据处理转向复杂场景应用，未来研究可能的进阶方向表现为数据格式的标准化、数据的合规性、临床试验的复杂场景应用等。
, authors=熊雪梅
1, 2, 顾锦
2, 胡晨希
2, 黄元楷
1, 徐厚明
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1Institute of Regulatory Science for Medical Products, China Pharmaceutical University, Nanjing 211198, China
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1中国药科大学药品监管科学研究院，南京 211198
2江苏省药品监督管理局审核查验中心，南京 210019, bio={"content":"
熊雪梅，女，硕士研究生，主要从事药物临床试验研究。联系电话：（025）86185036，E-mail: atmanxxm@163.com。
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熊雪梅，女，硕士研究生，主要从事药物临床试验研究。联系电话：（025）86185036，E-mail: atmanxxm@163.com。
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