REVIEW article
Front. Public Health
Sec. Digital Public Health
This article is part of the Research TopicAI and Mobile Technologies for Population-Level Chronic Disease PreventionView all 4 articles
Artificial Intelligence in Chronic Disease Self-Management: Current Applications and Future Directions
Provisionally accepted
- Affiliated Renhe Hospital Of China Three Gorges University, Yichang, China
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Objective: This study aims to summarize current applications of artificial intelligence (AI) for chronic disease self-management, critically appraise their effectiveness, and identify implementation challenges and future directions for research and clinical integration. Methods: A narrative literature review of peer-reviewed, English-language studies identified via PubMed, Web of Science, and Scopus was conducted, using combinations of “artificial intelligence,” “chronic disease,” “self-management,” “remote monitoring,” “predictive analytics,” “conversational agent,” and “mobile health.” Reference lists of key reviews were snowballed. We included studies that described or evaluated AI-enabled self-management tools or interventions for chronic conditions and excluded non-AI, acute-care, editorial, and non-human studies. Findings were synthesized thematically. Results: The literature consistently identifies four roles of AI in chronic care: (1) personalized decision support and treatment optimization; (2) continuous monitoring and risk prediction from patient-generated data; (3) conversational agents delivering education, adherence support, reminders, behavioral coaching, and mental-health support; and (4) AI-enabled Mobile health (mHealth) platforms that connect patients with clinicians and coordinate care. Recurrent challenges reported include data privacy and security risks, algorithmic bias and limited generalizability, interoperability and workflow-integration barriers, variable usability and sustained engagement (digital divide- inequalities in access to digital technologies and the internet, often influenced by age, income, or geography), and insufficient high-quality evidence on clinical effectiveness and cost-effectiveness. Conclusion: Future directions focus on developing more accurate, explainable, and trustworthy AI models, better clinical integration, leveraging advanced AI for engagement, rigorous evaluation, and addressing ethical and implementation barriers to realize AI's full potential in empowering patients and improving chronic disease outcomes.
Keywords: artificial intelligence, Chronic disease self-management, Personalized interventions, predictive analytics, Digital health platforms
Received: 21 Aug 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Du, Yang, Liu, Deng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ying Du, m13972517469@163.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.