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Article(id=1322937003330896699, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322936901946179634, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.10.009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1766505600000, receivedDateStr=2025-12-24, revisedDate=1776614400000, revisedDateStr=2026-04-20, acceptedDate=null, acceptedDateStr=null, onlineDate=1793342360574, onlineDateStr=2026-10-30, pubDate=1779638400000, pubDateStr=2026-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793342360574, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793342360574, creator=13701087609, updateTime=1793342360574, updator=13701087609, issue=Issue{id=1322936901946179634, tenantId=1146029695717560320, journalId=1220038315760373764, year='2026', volume='40', issue='10', pageStart='1617', pageEnd='1808', issueExtLink='null', onlineDate='null', pubDate='1779638400000', pubDateStr='2026-05-25', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1793342336403, creator='13701087609', updateTime=1793343127243, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext={EN=IssueExt(id=1322940219045052525, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322936901946179634, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1322940219045052526, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322936901946179634, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null, downloadFileDto=null}, startPage=1679, endPage=1685, ext={EN=ArticleExt(id=1322937003750327101, articleId=1322937003330896699, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Research hotspots and development trends of artificial intelligence empowering chronic disease prevention and control, columnId=1220689487877821128, journalTitle=Chinese Nursing Research, columnName=Interdisciplinary Nursing Study, runingTitle=null, highlight=null, articleAbstract=
Objective

To explore the research status quo,hotspots,and development trends of artificial intelligence in global chronic disease prevention and control.

Methods

Relevant studies on artificial intelligence empowering chronic disease prevention and control were retrieved from the Science Citation Index Expanded of Web of Science.The literature was analyzed using R-bibliometrix and VOSviewer.

Results

A total of 624 articles related to artificial intelligence empowering chronic disease prevention and control published between 2016 and 2025 were retrieved.High-frequency technology keywords included "machine learning" "deep learning" "Internet of things" and so on.High-frequency chronic disease keywords included "diabetes" "COVID-19" "cardiovascular diseases" and so on.Emerging technology keywords were "large language models" "long short-term memory networks" and so on.Emerging disease keywords were "asthma" and so on.

Conclusions

Artificial intelligence algorithms and the Internet of things constitute the core supporting technologies in the field of artificial intelligence empowering chronic disease prevention and control.Diabetes,COVID-19,and cardiovascular diseases are the focal diseases.Large language models and long short-term memory networks represent emerging technological directions.Asthma is an emerging hotspot disease.Artificial intelligence has played an important role in the field of chronic disease prevention and control.

, authors=Jingdong OU, Wanyi CHEN, Hongfang SHAO, authorsList=Jingdong OU, Wanyi CHEN, Hongfang SHAO, authorCompany=null, correspAuthors=Hongfang SHAO, 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=1322937007101576009, articleId=1322937003330896699, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=人工智能赋能慢性病防控的研究热点与发展趋势, columnId=1220689488053981904, journalTitle=护理研究, columnName=交叉护理学研究, runingTitle=null, highlight=null, articleAbstract=
目的

探索人工智能在慢性病防控中的研究现状、热点及发展趋势。

方法

检索Web of Science科学引文索引扩展版中人工智能赋能慢性病防控的相关研究，运用R-bibliometrix和VOSviewer对文献进行分析。

结果

共检索获得2016—2025年人工智能赋能慢性病防控相关文献624篇，高频技术关键词为“机器学习”“深度学习”“物联网”等，高频慢性病关键词为“糖尿病”“新型冠状病毒感染”“心血管疾病”等，新兴技术关键词为“大语言模型”“长短期记忆网络”等，新兴病种关键词为“哮喘”等。

结论

人工智能算法与物联网构成了人工智能赋能慢性病防控领域的核心支撑技术，糖尿病、新型冠状病毒感染以及心血管疾病为焦点病种，大语言模型与长短期记忆网络为新兴技术方向，哮喘为新兴热点病种。人工智能已在慢性病防控领域发挥了重要作用。

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欧敬东，护师，硕士研究生在读

, correspAuthorsNote=
邵红芳，E-mail:
SHAO Hongfang, E-mail:
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欧敬东，护师，硕士研究生在读

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欧敬东，护师，硕士研究生在读

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data-driven health portraits for personalized management in noncommunicable diseases:scoping review, refAbstract=null), Reference(id=1322937025481016206, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2022, volume=148, issue=null, pageStart=110164, pageEnd=null, url=null, language=null, rfNumber=21, rfOrder=20, authorNames=CAU R, FAA G, NARDI V, journalName=European Journal of Radiology, refType=null, unstructuredReference=CAU R,FAA G,NARDI V,et al.Long-COVID diagnosis:from diagnostic to advanced AI-driven models[J].European Journal of Radiology,2022,148:110164., articleTitle=Long-COVID diagnosis:from diagnostic to advanced AI-driven models, refAbstract=null), Reference(id=1322937025539736463, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2023, volume=147, issue=8, pageStart=E93, pageEnd=E621, url=null, language=null, rfNumber=22, rfOrder=21, authorNames=TSAO C W, ADAY A W, ALMARZOOQ Z I, journalName=Circulation, refType=null, unstructuredReference=TSAO C W,ADAY A W,ALMARZOOQ Z I,et al.Heart disease and stroke statistics-2023 update:a report from the American Heart Association[J].Circulation,2023,147(8):E93-E621., articleTitle=Heart disease and stroke statistics-2023 update:a report from the American Heart Association, refAbstract=null), Reference(id=1322937025862697872, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2025, volume=195, issue=null, pageStart=110621, pageEnd=null, url=null, language=null, rfNumber=23, rfOrder=22, authorNames=CHANG Y C, HSIAO S H, YEH W C, journalName=Computers in Biology and Medicine, refType=null, unstructuredReference=CHANG Y C,HSIAO S H,YEH W C,et al.Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models[J].Computers in Biology and Medicine,2025,195:110621., articleTitle=Extracting critical clinical indicators and survival prediction of lung cancer from pathology reports using large language models, refAbstract=null), Reference(id=1322937025925612434, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2023, volume=11, issue=6, pageStart=1664, pageEnd=null, url=null, language=null, rfNumber=24, rfOrder=23, authorNames=LIAO C M, SU C T, HUANG H C, journalName=Biomedicines, refType=null, unstructuredReference=LIAO C M,SU C T,HUANG H C,et al.Improved survival analyses based on characterized time-dependent covariates to predict individual chronic kidney disease progression[J].Biomedicines,2023,11(6):1664., articleTitle=Improved survival analyses based on characterized time-dependent covariates to predict individual chronic kidney disease progression, refAbstract=null), Reference(id=1322937026294711188, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2025, volume=261, issue=null, pageStart=125548, pageEnd=null, url=null, language=null, rfNumber=25, rfOrder=24, authorNames=HAMIDOU CISSOKO M BEN, CASTELAIN V, LACHICHE N, journalName=Expert Systems with Applications, refType=null, unstructuredReference=HAMIDOU CISSOKO M BEN,CASTELAIN V,LACHICHE N.Multi-way adaptive time aware LSTM for irregularly collected sequential ICU data[J].Expert Systems with Applications,2025,261:125548., articleTitle=Multi-way adaptive time aware LSTM for irregularly collected sequential ICU data, refAbstract=null), Reference(id=1322937026676392854, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2023, volume=401, issue=10379, pageStart=858, pageEnd=873, url=null, language=null, rfNumber=26, rfOrder=25, authorNames=PORSBJERG C, MELÉN E, LEHTIMÄKI L, journalName=Lancet, refType=null, unstructuredReference=PORSBJERG C,MELÉN E,LEHTIMÄKI L,et al.Asthma[J].Lancet,2023,401(10379):858-873., articleTitle=Asthma, refAbstract=null), Reference(id=1322937026743501719, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, doi=null, pmid=null, pmcid=null, year=2025, volume=15, issue=7, pageStart=3609, pageEnd=null, url=null, language=null, rfNumber=27, rfOrder=26, authorNames=ALKOBAISI S, SAFDAR M F, PALKA P, journalName=Applied Sciences-Basel, refType=null, unstructuredReference=ALKOBAISI S,SAFDAR M F,PALKA P,et al.Artificial intelligence algorithms in asthma management:a review of data engineering,predictive models,and future implications[J].Applied Sciences-Basel,2025,15(7):3609., articleTitle=Artificial intelligence algorithms in asthma management:a review of data engineering,predictive models,and future implications, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1322937007869133642, 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articleId=1322937003330896699, language=CN, label=图4, caption=关键词共现叠加时序覆盖分析, figureFileSmall=JRZgFzC/ako44gQs1Izvxw==, figureFileBig=6HEY1idf+L9xvN21iW9wiQ==, tableContent=null), ArticleFig(id=1322937015947363188, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, language=EN, label=Table 1, caption=

Top 10 most cited articles

, figureFileSmall=null, figureFileBig=null, tableContent=
排名第一作者发表时间累计被引频次（次）年均被引频次（次）科研机构
1Jelena Kornej2020年888148波士顿大学
2Darby Miller2018年37447奥古斯塔大学
3Yogesh Kumar2022年35890印第斯大学
4Yee Hui Yeo2023年342114西达赛奈医疗中心
5Ivan Contreras2018年27034赫罗纳大学
6Chao Yu2023年22575中山大学
7Melanie Lyons2020年21536俄亥俄州立大学
8Mirza Baig2017年18521奥克兰理工大学
9Francesco Casalegno2019年17325洛桑联邦理工学院
10Albert Haque2020年16928斯坦福大学
), ArticleFig(id=1322937016199021429, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, language=CN, label=表1, caption=

被引频次较多的10篇文献

, figureFileSmall=null, figureFileBig=null, tableContent=
排名第一作者发表时间累计被引频次（次）年均被引频次（次）科研机构
1Jelena Kornej2020年888148波士顿大学
2Darby Miller2018年37447奥古斯塔大学
3Yogesh Kumar2022年35890印第斯大学
4Yee Hui Yeo2023年342114西达赛奈医疗中心
5Ivan Contreras2018年27034赫罗纳大学
6Chao Yu2023年22575中山大学
7Melanie Lyons2020年21536俄亥俄州立大学
8Mirza Baig2017年18521奥克兰理工大学
9Francesco Casalegno2019年17325洛桑联邦理工学院
10Albert Haque2020年16928斯坦福大学
), ArticleFig(id=1322937016333239158, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, language=EN, label=Table 2, caption=

Top 20 high-frequency keywords(excluding search terms)

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关键词出现频次（次）关键词出现频次（次）
machine learning120predictive models20
deep learning72eHealth20
internet of things33precision medicine18
diabetes31big data18
digital health30COVID-1918
telemedicine28diagnosis17
chronic disease management25cardiovascular disease17
wearable device24personalized medicine14
healthcare24electronic health records13
mHealth24remote monitoring13
), ArticleFig(id=1322937016610063223, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, language=CN, label=表2, caption=

出现频次较高的20个关键词（排除检索词）

, figureFileSmall=null, figureFileBig=null, tableContent=
关键词出现频次（次）关键词出现频次（次）
machine learning120predictive models20
deep learning72eHealth20
internet of things33precision medicine18
diabetes31big data18
digital health30COVID-1918
telemedicine28diagnosis17
chronic disease management25cardiovascular disease17
wearable device24personalized medicine14
healthcare24electronic health records13
mHealth24remote monitoring13
), ArticleFig(id=1322937017474089848, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, language=EN, label=Table 3, caption=

Top 11 high-timeliness keywords(by average publication year)

, figureFileSmall=null, figureFileBig=null, tableContent=
关键词平均出版年出现频次（次）
large language model2024年6
health management2024年5
long short term memory2024年5
ChatGPT2024年9
asthma2023年7
bibliometrics2023年7
multimorbidity2023年5
pediatrics2023年5
systematic review2023年8
accuracy2023年7
public health2023年7
), ArticleFig(id=1322937017864160121, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322937003330896699, language=CN, label=表3, caption=

11个高时效性关键词（按平均出版年）

, figureFileSmall=null, figureFileBig=null, tableContent=
关键词平均出版年出现频次（次）
large language model2024年6
health management2024年5
long short term memory2024年5
ChatGPT2024年9
asthma2023年7
bibliometrics2023年7
multimorbidity2023年5
pediatrics2023年5
systematic review2023年8
accuracy2023年7
public health2023年7
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人工智能赋能慢性病防控的研究热点与发展趋势
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欧敬东 , 陈婉怡 , 邵红芳 *, *
护理研究 | 交叉护理学研究 2026,40(10): 1679-1685
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护理研究 |交叉护理学研究 2026 , 40 (10) : 1679 -1685
人工智能赋能慢性病防控的研究热点与发展趋势
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欧敬东, 陈婉怡, 邵红芳*, *
作者信息
  • 1山西医科大学，山西 030000
通讯作者:
邵红芳，E-mail:
SHAO Hongfang, E-mail:
作者简介:

欧敬东，护师，硕士研究生在读

Research hotspots and development trends of artificial intelligence empowering chronic disease prevention and control
Jingdong OU, Wanyi CHEN, Hongfang SHAO
Affiliations
  • 1Shanxi Medical University, Shanxi 030000 China
出版时间: 2026-05-25 doi: 10.12102/j.issn.1009-6493.2026.10.009
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摘要
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目的

探索人工智能在慢性病防控中的研究现状、热点及发展趋势。

方法

检索Web of Science科学引文索引扩展版中人工智能赋能慢性病防控的相关研究，运用R-bibliometrix和VOSviewer对文献进行分析。

结果

共检索获得2016—2025年人工智能赋能慢性病防控相关文献624篇，高频技术关键词为“机器学习”“深度学习”“物联网”等，高频慢性病关键词为“糖尿病”“新型冠状病毒感染”“心血管疾病”等，新兴技术关键词为“大语言模型”“长短期记忆网络”等，新兴病种关键词为“哮喘”等。

结论

人工智能算法与物联网构成了人工智能赋能慢性病防控领域的核心支撑技术，糖尿病、新型冠状病毒感染以及心血管疾病为焦点病种，大语言模型与长短期记忆网络为新兴技术方向，哮喘为新兴热点病种。人工智能已在慢性病防控领域发挥了重要作用。

慢性病  /  人工智能  /  研究热点  /  趋势  /  可视化  /  文献计量学  /  关键词
Objective

To explore the research status quo,hotspots,and development trends of artificial intelligence in global chronic disease prevention and control.

Methods

Relevant studies on artificial intelligence empowering chronic disease prevention and control were retrieved from the Science Citation Index Expanded of Web of Science.The literature was analyzed using R-bibliometrix and VOSviewer.

Results

A total of 624 articles related to artificial intelligence empowering chronic disease prevention and control published between 2016 and 2025 were retrieved.High-frequency technology keywords included "machine learning" "deep learning" "Internet of things" and so on.High-frequency chronic disease keywords included "diabetes" "COVID-19" "cardiovascular diseases" and so on.Emerging technology keywords were "large language models" "long short-term memory networks" and so on.Emerging disease keywords were "asthma" and so on.

Conclusions

Artificial intelligence algorithms and the Internet of things constitute the core supporting technologies in the field of artificial intelligence empowering chronic disease prevention and control.Diabetes,COVID-19,and cardiovascular diseases are the focal diseases.Large language models and long short-term memory networks represent emerging technological directions.Asthma is an emerging hotspot disease.Artificial intelligence has played an important role in the field of chronic disease prevention and control.

chronic diseases  /  artificial intelligence  /  research hotspots  /  trends  /  visualization  /  bibliometrics  /  keywords
欧敬东, 陈婉怡, 邵红芳. 人工智能赋能慢性病防控的研究热点与发展趋势. 护理研究, 2026 , 40 (10) : 1679 -1685 . DOI: 10.12102/j.issn.1009-6493.2026.10.009
Jingdong OU, Wanyi CHEN, Hongfang SHAO. Research hotspots and development trends of artificial intelligence empowering chronic disease prevention and control[J]. Chinese Nursing Research, 2026 , 40 (10) : 1679 -1685 . DOI: 10.12102/j.issn.1009-6493.2026.10.009
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慢性病（chronic disease）主要包括心血管疾病（cardiovascular disease，CVD）、癌症（cancer）和慢性呼吸系统疾病（chronic respiratory diseases，CRD）等[1]。世界卫生组织（World Health Organization，WHO）和全球疾病负担（Global Burden of Disease Study，GBD）研究数据显示，慢性病导致的死亡人数在全球总死亡人数中的占比持续攀升，给各国卫生系统与经济发展带来沉重压力[2-3]。当前慢性病防控面临的核心挑战较多，如人口老龄化加剧导致病人基数持续扩大[4]；城乡与区域间医疗资源分配不均，基层及偏远地区管理能力薄弱[5]；疾病起病隐匿，早期筛查与诊断效率不足[6]；病人对长期治疗的依从性较低，自我管理困难[7]；中老年多病共存现象普遍，诊疗复杂性增加[8]。人工智能（artificial intelligence，AI）是一门致力于设计与构建能够模拟和拓展人类智能的计算机系统技术[9]。近年来随着算力的提升、算法的创新以及大数据的积累，AI在医疗健康领域的应用迅速拓展[10]。知识图谱因其直观性、科学性和系统性，可用在研究方向选择、研究前沿与热点分析以及学科发展态势分析、科技政策制定等方面[11]。本研究对全球近年来AI赋能慢性病防控的知识图谱进行研究，旨在厘清AI在全球慢性病防控诊疗中的应用现状、研究前沿与热点，以期为我国以及全球医疗卫生事业的高质量发展提供支撑与决策参考。
1　资料与方法
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1.1　文献检索
依据美国国家医学图书馆（National Library of Medicine，NLM）发布的医学主题词列表（Medical Subject Headings，MeSH）规范构建检索式，选取“chronic disease”“noncommunicable diseases”“artificial intelligence”为核心检索主题词，并全面纳入其同义词；检索Web of Science的科学引文索引扩展版（Science Citation Index Expanded，SCI-E）；检索时限为2016年5月30日—2025年6月13日；文献类型为研究论文及综述。
1.2　数据分析工具
提取文献作者、标题、来源出版物及引用文献等多维度元数据。采用BiblioMetrix R（v4.3.3）与VOSviewer（v1.6.20）进行文献计量分析。BiblioMetrix R包提供了从数据预处理到知识结构分析的完整功能，适合进行深度计量研究[12]。VOSviewer具有可视化效果和聚类功能，可用于构建合作、共现及共被引等网络图谱，以揭示研究热点与知识结构[13]。
2　结果
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2.1　年发文量
共检索获得2016—2025年AI赋能慢性病防控相关文献624篇，其发展可分为3个阶段：初始阶段（2016—2017年）年均发文仅2篇；持续加速阶段（2018—2021年）年发文量从11篇跃升至79篇；暴发阶段（2022—2025年），发文量持续提升，2023年突破百篇（109篇），2024年达138篇。见图1。
2.2　被引频次
波士顿大学Jelena Kornej团队2020年发表的论文累计被引频次最高，为888次。西达赛奈医疗中心Yee Hui Yeo团队与中山大学Chao Yu团队于2023年发表的2篇论文发表时间较短，但累计被引频次较高，分别为342次和225次，其对AI赋能慢性病防控领域的关键问题研究具有奠基性贡献与创新引领价值。见表1。
2.3　关键词分析
共收集2 046个关键词，经标准化处理后最终筛选获得75个高频关键词（出现频次>5次）。
2.3.1　关键词聚类分析
运用VOSviewer软件绘制网络图，图中节点代表关键词，节点大小代表词频，连边数量反映关键词关联广度，连线粗细代表特定两个关键词间的共现强度，同色系节点代表同一主题群落。AI赋能慢性病防控研究被聚类为6个主题，聚类1（红色，16个关键词）以“慢性病（chronic disease）”为核心关键词，同时还包括“糖尿病（diabetes）”“预测模型（predictive models）”以及“诊断（diagnosis）”等关键词，该聚类的研究主题可归纳为基于数据挖掘与生物信息学的多病共存风险分层与临床决策支持模型研究。聚类2（绿色，14个关键词）以“物联网（internet of things）”为核心，同时还包括“可穿戴设备（wearable device）”“精准医学（precision medicine）”与“心血管疾病（cardiovascular disease）”等关键词，该聚类的研究主题可归纳为基于可穿戴设备物联网与可解释AI的心血管疾病精准预警及个性化干预系统研究。聚类3（蓝色，13个关键词）以“人工智能（artificial intelligence）”为核心，同时还包括“数字健康（digital health）”“医疗保健（healthcare）”与“电子健康（eHealth）”等关键词，该聚类的研究主题可归纳为后疫情时代AI驱动的非传染性疾病防控体系重构。见图2。
2.3.2　高频关键词分析
纳入的关键词中，“机器学习（machine learning）”出现频次最高，“深度学习（deep learning）”与“物联网（internet of things）”次之，出现频次较高的20个关键词见表2。
2.3.3　病种关键词分析
在前20个高频关键词中，“糖尿病（diabetes）”是出现最多的慢性病病种关键词，其后是“新型冠状病毒感染（COVID-19）”与“心血管疾病（cardiovascular disease）”。本研究进一步挖掘全量关键词数据集发现，“癌症（cancer）”“2型糖尿病（type 2 diabetes）”及“肥胖症（obesity）”等关键词同样呈现高频特征，见图3。
2.3.4　新兴关键词分析
通过VOSviewer的时序叠加功能进一步绘制75个高频关键词的平均出版年热力图，关键词的平均出版年是指包含该关键词的所有文献出版年份的算术平均值，用于衡量该关键词相关研究的整体时效性。图中暖色调节点代表高平均出版年热力值，即新兴研究方向；冷色调节点代表低平均出版年热力值，即传统研究方向。结果显示，“大语言模型（large language model）”“健康管理（health management）”“长短期记忆网络（long short term memory）”的平均出版年热力值较高，属于较显著的新兴研究方向。其后为“ChatGPT”“哮喘（asthma）”等关键词，见图4、表3。
3　讨论
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当前全球慢性病防控压力剧增，传统管理模式存在局限，AI的发展为体系革新带来契机。本研究采用科学计量方法，系统梳理2016—2025年AI赋能慢性病防控的相关论文，旨在描绘该领域的发展全貌，为未来研究提供指引。
3.1　全球研究态势与焦点
近年来，AI赋能慢性病防控领域研究的发展分为3个阶段，2016—2017年，年均发文仅2篇，尚处于初始阶段；2018年后论文增长速度加快，2018—2021年的年发文量从11篇跃升至79篇；2022年及以后，发展进入暴发阶段，2023年的发文量首次突破百篇，表明该领域研究规模正从积累向深化转型。
波士顿大学Jelena Kornej团队2020年发表的论文累计被引频次最高（888次），说明其研究成果在学术界获得广泛认可。该研究系统探讨了21世纪心房颤动的全球流行趋势、可改变风险因素的新证据及其剂量效应，剖析了心房颤动与多种系统性疾病间的双向病理关联，并前瞻性地评估了AI与多组学技术在精准预防与管理中的应用潜力[14]。此外，西达赛奈医疗中心Yee Hui Yeo团队与中山大学Chao Yu团队均于2023年发表1篇论文，获得大量关注。Yee Hui Yeo团队的研究首次评估了ChatGPT在肝硬化与肝细胞癌病人管理中的表现，证实ChatGPT能准确回答多数常见问题并提供情感支持，但也揭示了其在诊断与预防知识、临床决策阈值及地域指南适应性方面的局限，明确了其作为辅助工具的潜力与改进方向[15]。Chao Yu团队的研究首次系统梳理了强化学习在医疗健康领域的应用框架、技术与案例，深入分析了强化学习在医疗场景下面临的独特挑战，并指明可解释策略、小样本学习及临床转化等未来发展方向，为跨领域研究者提供了较全面的技术路线图[16]。
3.2　研究热点及前沿
在AI赋能慢性病防控研究中，“机器学习”与“深度学习”是出现频次最高的关键词。机器学习凭借模型可解释性强、对数据量要求相对灵活的特点，已广泛应用于疾病风险预测、早期筛查及并发症预警等方面[17]。深度学习以强大的特征自动提取与模式识别能力，在医学影像分析、多模态数据融合以及从海量数据中发现复杂关联等方面表现突出[18]。“物联网”出现频次仅次于“机器学习”与“深度学习”，它可通过连接可穿戴设备、家用医疗传感器等实现对慢性病病人生理参数的连续、实时监测[19]。物联网+AI为构建个体化、高分辨率的健康画像奠定了坚实基础，从而极大赋能于慢性病的长期远程监护、病情动态追踪、用药依从性管理及生活方式干预等客观评估，二者共同推动了慢性病管理向智能化、精准化迈进[20]。
“糖尿病”是出现最多的慢性病病种关键词，其出现频次高于“新型冠状病毒感染”与“心血管疾病”。作为全球负担沉重的慢性病，糖尿病的管理亟须智能化方案，这使其成为AI赋能慢性病防控领域研究的核心病种。新型冠状病毒感染为急性传染病，但病人康复后会出现长新冠综合征[21]，构成了新的慢性病态，如何利用AI对其进行预测与管理，成为重要的新兴研究方向。心血管疾病是全球首要死因和慢性病病人过早死亡的主要原因之一，其发生、发展与多种可干预的风险因素密切相关[22]。传统防控模式在应对该疾病的隐匿性与复杂性方面存在不足，尤其在风险预测与个性化管理等方面效果不佳，而AI的发展为问题的解决提供了新路径。
本研究结合新兴关键词探索AI赋能慢性病防控领域的前沿，结果显示“大语言模型”“健康管理”及“长短期记忆网络”的平均出版年热力值较高，“ChatGPT”“哮喘”等也具有强时效性。传统自然语言处理方法在整合医患交互、治疗决策等多源异构知识时存在不足，难以有效建模医疗文本中的复杂逻辑关系[23]，而大语言模型凭借其深层语义理解与指令微调能力，能从海量非结构化数据中提取知识，为构建慢性病智能决策支持系统提供框架。慢性病的病程发展具有时间依赖性[24]，传统模型对此类时序数据的分析能力有限，而长短期记忆网络作为循环神经网络的关键变体，能有效应对慢性病数据的时间不规律性与多模态融合挑战[25]。哮喘近年来成为新兴研究病种，其作为全球高发的慢性呼吸系统疾病，具有环境暴露敏感性与动态波动特性，亟需实时数据驱动干预，传统管理范式对此作用有限[26]，而AI能通过整合环境传感器数据、可穿戴设备实时监测结果及病人主诉信息，精准预警急性发作风险并识别个体化触发因子[27]。
4　小结
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AI赋能慢性病防控领域的研究热点与前沿分析可以展示该领域近年最活跃的研究方向，能为研究人员探索新思路、发掘有价值的科研选题提供指引，也能为决策者在该领域的科研资源优化配置与战略布局提供重要的数据支持。

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2026年第40卷第10期
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doi: 10.12102/j.issn.1009-6493.2026.10.009
  • 接收时间：2025-12-24
  • 首发时间：2026-10-30
  • 出版时间：2026-05-25
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  • 收稿日期：2025-12-24
  • 修回日期：2026-04-20
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    1山西医科大学，山西 030000

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邵红芳，E-mail:
SHAO Hongfang, E-mail:
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
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