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已发表论文
基于人工智能的 2 型糖尿病患者健康教育干预措施的范围综述
Authors Li C, Li W, Shao Y, Xu Z, Song J, Wang Y
Received 11 June 2025
Accepted for publication 28 August 2025
Published 19 September 2025 Volume 2025:18 Pages 3539—3552
DOI https://doi.org/10.2147/DMSO.S541515
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Prof. Dr. Ernesto Maddaloni
Chen Li,1 Wei Li,2 Yanru Shao,2 Zhigang Xu,3 Junyan Song,1 Yan Wang1
1School of Nursing, Jining Medical University, Jining, 272067, People’s Republic of China; 2Nursing Department, The Affiliated Hospital of Jining Medical College, Jining, 272000, People’s Republic of China; 3School of Graduate, Shandong First Medical University, Jinan, 250000, People’s Republic of China
Correspondence: Junyan Song, School of Nursing, Jining Medical University, Jining, 272067, People’s Republic of China, Tel +86 13791798026, Email 13791798026@126.com Yan Wang, School of Nursing, Jining Medical University, Jining, 272067, People’s Republic of China, Tel +86 15965739797, Email wangyan7230@sina.com
Background: Type 2 diabetes mellitus (T2DM) poses a critical global health burden, requiring effective health education to enhance patient self-management. Artificial intelligence (AI) offers personalized and scalable solutions; however, comprehensive syntheses of its applications in T2DM health education are scarce.
Objective: Guided by the Arksey and O’Malley scoping review framework, this study maps AI-based health education interventions for T2DM by evaluating technologies, effectiveness, and challenges.
Methods: Seven academic databases (PubMed, Web of Science, Embase, Scopus, EBSCO, the Cochrane Library, the Joanna Briggs Institute (JBI) Database, and Wiley Online Library) were searched for studies published from 2008 to March 2025, identifying 14 eligible interventional studies involving 32,478 adult T2DM patients receiving AI-based health education.
Results: (1) Technological Diversity: Interventions included mobile apps (eg, FoodLens, TRIO system), chatbots, intelligent platforms, and machine learning algorithms, focusing on diet, glucose monitoring, and lifestyle management. (2) Effectiveness: AI interventions enhanced glycemic control, yielding reductions in glycosylated hemoglobin (HbA1c) of up to 2.59%, improved self management adherence (60– 85%), and produced positive psychological outcomes (eg, increased self efficacy); efficacy varied by intervention duration and user engagement. (3) Challenges: Key barriers included technical complexity, low long-term engagement, digital literacy gaps, and data privacy concerns.
Conclusion: AI holds substantial potential for T2DM health education via personalized, accessible interventions. Future research should address technological hurdles, prioritize user-centered design, and integrate AI into healthcare systems to ensure sustainability and equity.
Keywords: T2DM, AI, health education, scoping review
1School of Nursing, Jining Medical University, Jining, 272067, People’s Republic of China; 2Nursing Department, The Affiliated Hospital of Jining Medical College, Jining, 272000, People’s Republic of China; 3School of Graduate, Shandong First Medical University, Jinan, 250000, People’s Republic of China
Correspondence: Junyan Song, School of Nursing, Jining Medical University, Jining, 272067, People’s Republic of China, Tel +86 13791798026, Email 13791798026@126.com Yan Wang, School of Nursing, Jining Medical University, Jining, 272067, People’s Republic of China, Tel +86 15965739797, Email wangyan7230@sina.com
Background: Type 2 diabetes mellitus (T2DM) poses a critical global health burden, requiring effective health education to enhance patient self-management. Artificial intelligence (AI) offers personalized and scalable solutions; however, comprehensive syntheses of its applications in T2DM health education are scarce.
Objective: Guided by the Arksey and O’Malley scoping review framework, this study maps AI-based health education interventions for T2DM by evaluating technologies, effectiveness, and challenges.
Methods: Seven academic databases (PubMed, Web of Science, Embase, Scopus, EBSCO, the Cochrane Library, the Joanna Briggs Institute (JBI) Database, and Wiley Online Library) were searched for studies published from 2008 to March 2025, identifying 14 eligible interventional studies involving 32,478 adult T2DM patients receiving AI-based health education.
Results: (1) Technological Diversity: Interventions included mobile apps (eg, FoodLens, TRIO system), chatbots, intelligent platforms, and machine learning algorithms, focusing on diet, glucose monitoring, and lifestyle management. (2) Effectiveness: AI interventions enhanced glycemic control, yielding reductions in glycosylated hemoglobin (HbA1c) of up to 2.59%, improved self management adherence (60– 85%), and produced positive psychological outcomes (eg, increased self efficacy); efficacy varied by intervention duration and user engagement. (3) Challenges: Key barriers included technical complexity, low long-term engagement, digital literacy gaps, and data privacy concerns.
Conclusion: AI holds substantial potential for T2DM health education via personalized, accessible interventions. Future research should address technological hurdles, prioritize user-centered design, and integrate AI into healthcare systems to ensure sustainability and equity.
Keywords: T2DM, AI, health education, scoping review