@Article{info:doi/10.2196/59632,
author="Hwang, Misun
and Zheng, Yaguang
and Cho, Youmin
and Jiang, Yun",
title="AI Applications for Chronic Condition Self-Management: Scoping Review",
journal="J Med Internet Res",
year="2025",
month="Apr",
day="8",
volume="27",
pages="e59632",
keywords="artificial intelligence; chronic disease; self-management; generative AI; emotional self-management",
abstract="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. ",
issn="1438-8871",
doi="10.2196/59632",
url="https://www.jmir.org/2025/1/e59632",
url="https://doi.org/10.2196/59632"
}