Article(id=1322936912020897940, tenantId=1146029695717560320, journalId=1220038315760373764, issueId=1322936901946179634, articleNumber=null, orderNo=null, doi=10.12102/j.issn.1009-6493.2026.10.025, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1744300800000, receivedDateStr=2025-04-11, revisedDate=1770912000000, revisedDateStr=2026-02-13, acceptedDate=null, acceptedDateStr=null, onlineDate=1793342338804, onlineDateStr=2026-10-30, pubDate=1779638400000, pubDateStr=2026-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1793342338804, onlineIssueDateStr=2026-10-30, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1793342338804, creator=13701087609, updateTime=1793342338804, 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=1788, endPage=1793, ext={EN=ArticleExt(id=1322936913727979669, articleId=1322936912020897940, tenantId=1146029695717560320, journalId=1220038315760373764, language=EN, title=Application of large language models in the field of chronic disease self-management:a scoping review, columnId=1220689485315101342, journalTitle=Chinese Nursing Research, columnName=Review Articles, runingTitle=null, highlight=null, articleAbstract=
Objective To conduct a scoping review of the application of large language models in the field of chronic disease self-management,and to provide a reference for future research.
Methods The Joanna Briggs Institute scoping review guideline was used as the framework.Databases including CNKI,Wanfang,VIP,PubMed,EMbase,and Web of Science Core Collection were searched.Literature screening and data extraction were performed.
Results A total of 10 articles were included.The summarized information indicates that the application forms of large language models in the field of chronic disease self-management included chatbots,intelligent interactive information support systems,precision recommendation systems integrated with medical data,and integrated image-language systems.The content elements included providing disease knowledge and self-management skills guidance,exercise management,diet management,medication management,activities of daily living management,health data monitoring,communication with healthcare professionals,and psychological support.The main application outcomes was related to self-management ability indicators,symptoms indicators,feasibility indicators,and other indicators.
Conclusions Large language models can enhance patients' self-management abilities,symptom indicators and psychological states.Large language models demonstrate high feasibility and patient acceptance.However,existing studies have limitations such as insufficient intervention depth,limited coverage of disease types,and ethical risks.Future efforts should optimize model functional design based on professional characteristics,strengthen ethical and privacy protection mechanisms,and explore the sustainability of large language models in chronic disease management to promote patient recovery and improve their quality of life.
, authors=Shuai ZHANG, Han WANG, Kexin ZHANG, Liping ZHANG, Jiazhen LIU, Xiaoxing LAI, authorsList=Shuai ZHANG, Han WANG, Kexin ZHANG, Liping ZHANG, Jiazhen LIU, Xiaoxing LAI, authorCompany=null, correspAuthors=Xiaoxing LAI, 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=1322936914436817048, articleId=1322936912020897940, tenantId=1146029695717560320, journalId=1220038315760373764, language=CN, title=大语言模型在慢性病自我管理领域应用的范围综述, columnId=1220689485482873504, journalTitle=护理研究, columnName=科研综述, runingTitle=null, highlight=null, articleAbstract=
目的 对大语言模型在慢性病自我管理领域的应用进行范围综述，为开展后续研究提供参考。
方法 以澳大利亚乔安娜布里格斯研究所的范围综述指南为框架，检索中国知网（CNKI）、万方数据库、维普数据库、PubMed、EMbase、Web of Science核心数据库，并进行文献筛选及数据提取。
结果 共纳入10篇文献，汇总信息结果显示，大语言模型在慢性病自我管理中的应用形式包括聊天机器人、智能交互式信息支持系统、结合医疗数据的精准推荐系统、集成式图像-语言系统；内容要素包括提供疾病知识和自我管理技能指导、运动管理、饮食管理、服药管理、日常生活活动管理、健康数据监测、与医护人员沟通、提供心理支持；应用效果主要涉及自我管理能力指标、症状指标、可行性指标及其他指标。
结论 大语言模型可提升病人自我管理能力，改善症状指标和心理状态，具备较高的可行性与病人接受度。但现有研究存在干预深度不足、病种覆盖局限及伦理风险等问题。未来需结合专科特点优化模型功能设计，强化伦理与隐私保护机制，探索大语言模型在慢性病管理中的可持续性，以促进病人疾病康复，改善病人生活质量。
, authors=张帅, 王涵, 张可欣, 张丽萍, 刘佳珍, 赖小星
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1中国医学科学院北京协和医院，北京 100730)])], figs=[ArticleFig(id=1322936918631121090, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322936912020897940, language=EN, label=Figure 1, caption=
Literature screening process and results, figureFileSmall=bEaAxMRgrrkpCI37KIngtg==, figureFileBig=ZOn6Iz++eR7plVwDQsmUxA==, tableContent=null), ArticleFig(id=1322936918710812867, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322936912020897940, language=CN, label=图1, caption=
文献筛选流程及结果, figureFileSmall=bEaAxMRgrrkpCI37KIngtg==, figureFileBig=ZOn6Iz++eR7plVwDQsmUxA==, tableContent=null), ArticleFig(id=1322936918928916676, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322936912020897940, language=EN, label=Table 1, caption=
Basic characteristics of the included studies
, figureFileSmall=null, figureFileBig=null, tableContent=
| 纳入文献 | 国家 | 研究类型 | 研究场所 | 研究对象 | | 样本量（例） | | | 试验组干预方法 | | 对照组干预方法 | 结局指标 |
|---|
| 试验组 | 对照组 | LLMs类型 | 内容要素 |
|---|
| 蒋璐璐等[7]2023 | 中国 | 随机对照试验 | 门诊、住院 | 乳腺癌病人 | 45 | 42 | 智能交互引擎 | ①②③ | 常规的信息支持 | 自我管理能力指标、症状指标、其他指标 |
| He等[8]2022 | 中国 | 随机对照试验 | 居家 | 抑郁症病人 | 49 | 对照组1：49对照组2：50 | chatbot | ④ | 电子书；普通聊天机器人 | 症状指标、可行性指标 |
| Anan等[9]2021 | 日本 | 随机对照试验 | 居家 | 慢性疼痛病人 | 48 | 46 | chatbot | ①⑤ | 常规活动 | 症状指标、可行性指标 |
| Kaphingst等[10]2024 | 美国 | 随机对照试验 | 门诊 | 癌症病人 | 1 554 | 1 519 | chatbot | ⑥ | 常规护理随访 | 症状指标、可行性指标、其他指标 |
| Ulrich等[11]2024 | 德国 | 随机对照试验 | 居家 | 慢性疼痛病人 | 110 | 88 | MobileCoach | ①②③④ | 常规护理随访 | 自我管理能力指标、症状指标、可行性指标 |
| Jang等[12]2021 | 韩国 | 随机对照试验 | 居家 | 注意力缺陷病人 | 23 | 23 | chatbot | ①④ | 纸质书 | 症状指标、可行性指标 |
| Jonassaint等[13]2024 | 美国 | 随机对照试验 | 居家 | 慢性疼痛病人 | 181 | 178 | chatbot | ①④ | 常规护理 | 自我管理能力指标、症状指标、可行性指标、其他指标 |
| Yasukawa等[14]2024 | 日本 | 随机对照试验 | 居家 | 抑郁症病人 | 70 | 73 | chatbot | ①②④ | 常规认知行为疗法 | 症状指标、可行性指标、其他指标 |
| Kelly等[15]2025 | 英国 | 随机对照试验 | 居家 | 糖尿病病人 | 49 | 49 | Spotlight-AQ | ①③④⑥ | 常规护理 | 症状指标、可行性指标 |
| Li等[16]2024 | 中国 | 类实验研究 | 门诊 | 糖尿病病人 | 372 | 397 | language system-DeepDR-LLM | ⑤⑦⑧ | 初级卫生保健人员 | 自我管理能力指标、症状指标、可行性指标、其他指标 |
), ArticleFig(id=1322936919008608453, tenantId=1146029695717560320, journalId=1220038315760373764, articleId=1322936912020897940, language=CN, label=表1, caption=
纳入文献的基本特征
, figureFileSmall=null, figureFileBig=null, tableContent=
| 纳入文献 | 国家 | 研究类型 | 研究场所 | 研究对象 | | 样本量（例） | | | 试验组干预方法 | | 对照组干预方法 | 结局指标 |
|---|
| 试验组 | 对照组 | LLMs类型 | 内容要素 |
|---|
| 蒋璐璐等[7]2023 | 中国 | 随机对照试验 | 门诊、住院 | 乳腺癌病人 | 45 | 42 | 智能交互引擎 | ①②③ | 常规的信息支持 | 自我管理能力指标、症状指标、其他指标 |
| He等[8]2022 | 中国 | 随机对照试验 | 居家 | 抑郁症病人 | 49 | 对照组1：49对照组2：50 | chatbot | ④ | 电子书；普通聊天机器人 | 症状指标、可行性指标 |
| Anan等[9]2021 | 日本 | 随机对照试验 | 居家 | 慢性疼痛病人 | 48 | 46 | chatbot | ①⑤ | 常规活动 | 症状指标、可行性指标 |
| Kaphingst等[10]2024 | 美国 | 随机对照试验 | 门诊 | 癌症病人 | 1 554 | 1 519 | chatbot | ⑥ | 常规护理随访 | 症状指标、可行性指标、其他指标 |
| Ulrich等[11]2024 | 德国 | 随机对照试验 | 居家 | 慢性疼痛病人 | 110 | 88 | MobileCoach | ①②③④ | 常规护理随访 | 自我管理能力指标、症状指标、可行性指标 |
| Jang等[12]2021 | 韩国 | 随机对照试验 | 居家 | 注意力缺陷病人 | 23 | 23 | chatbot | ①④ | 纸质书 | 症状指标、可行性指标 |
| Jonassaint等[13]2024 | 美国 | 随机对照试验 | 居家 | 慢性疼痛病人 | 181 | 178 | chatbot | ①④ | 常规护理 | 自我管理能力指标、症状指标、可行性指标、其他指标 |
| Yasukawa等[14]2024 | 日本 | 随机对照试验 | 居家 | 抑郁症病人 | 70 | 73 | chatbot | ①②④ | 常规认知行为疗法 | 症状指标、可行性指标、其他指标 |
| Kelly等[15]2025 | 英国 | 随机对照试验 | 居家 | 糖尿病病人 | 49 | 49 | Spotlight-AQ | ①③④⑥ | 常规护理 | 症状指标、可行性指标 |
| Li等[16]2024 | 中国 | 类实验研究 | 门诊 | 糖尿病病人 | 372 | 397 | language system-DeepDR-LLM | ⑤⑦⑧ | 初级卫生保健人员 | 自我管理能力指标、症状指标、可行性指标、其他指标 |
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