AUTHOR=Du Ying , Yang Peng , Liu Yuntao , Deng Chunxia , Li Xin 

TITLE=Artificial intelligence in chronic disease self-management: current applications and future directions

JOURNAL=Frontiers in Public Health

VOLUME=Volume 13 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2025.1689911

DOI=10.3389/fpubh.2025.1689911

ISSN=2296-2565

ABSTRACT=ObjectiveThis 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.MethodsA 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.ResultsThe 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.ConclusionFuture 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.