%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 27
%N 
%P e59632
%T AI Applications for Chronic Condition Self-Management: Scoping Review
%A Hwang,Misun
%A Zheng,Yaguang
%A Cho,Youmin
%A Jiang,Yun
%+ , School of Nursing, University of Michigan, 400 North Ingalls Street, Ann Arbor, MI, 48109, United States, 1 7347633705, jiangyu@umich.edu
%K artificial intelligence
%K chronic disease
%K self-management
%K generative AI
%K emotional self-management
%D 2025
%7 8.4.2025
%9 Review
%J J Med Internet Res
%G English
%X Background: Artificial intelligence (AI) has potential in promoting and supporting self-management in patients with chronic conditions. However, the development and application of current AI technologies to meet patients’ needs and improve their performance in chronic condition self-management tasks remain poorly understood. It is crucial to gather comprehensive information to guide the development and selection of effective AI solutions tailored for self-management in patients with chronic conditions. Objective: This scoping review aimed to provide a comprehensive overview of AI applications for chronic condition self-management based on 3 essential self-management tasks, medical, behavioral, and emotional self-management, and to identify the current developmental stages and knowledge gaps of AI applications for chronic condition self-management. Methods: A literature review was conducted for studies published in English between January 2011 and October 2024. In total, 4 databases, including PubMed, Web of Science, CINAHL, and PsycINFO, were searched using combined terms related to self-management and AI. The inclusion criteria included studies focused on the adult population with any type of chronic condition and AI technologies supporting self-management. This review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Results: Of the 1873 articles retrieved from the search, 66 (3.5%) were eligible and included in this review. The most studied chronic condition was diabetes (20/66, 30%). Regarding self-management tasks, most studies aimed to support medical (45/66, 68%) or behavioral self-management (27/66, 41%), and fewer studies focused on emotional self-management (14/66, 21%). Conversational AI (21/66, 32%) and multiple machine learning algorithms (16/66, 24%) were the most used AI technologies. However, most AI technologies remained in the algorithm development (25/66, 38%) or early feasibility testing stages (25/66, 38%). Conclusions: A variety of AI technologies have been developed and applied in chronic condition self-management, primarily for medication, symptoms, and lifestyle self-management. Fewer AI technologies were developed for emotional self-management tasks, and most AIs remained in the early developmental stages. More research is needed to generate evidence for integrating AI into chronic condition self-management to obtain optimal health outcomes. 
%R 10.2196/59632
%U https://www.jmir.org/2025/1/e59632
%U https://doi.org/10.2196/59632