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

作者：Ying Du, Peng Yang, Yuntao Liu, Chunxia Deng, Xin Li · 发表于：Frontiers in Public Health · 年份：2025 · DOI：10.3389/fpubh.2025.1689911 · 被引用次数：15 · 研究领域：Artificial Intelligence in Healthcare and Education、Digital Mental Health Interventions、Machine Learning in Healthcare

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, va...