AI Applications for Chronic Condition Self-Management: Scoping Review
BackgroundArtificial 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...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
JMIR Publications
2025-04-01
|
| Series: | Journal of Medical Internet Research |
| Online Access: | https://www.jmir.org/2025/1/e59632 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849703793578475520 |
|---|---|
| author | Misun Hwang Yaguang Zheng Youmin Cho Yun Jiang |
| author_facet | Misun Hwang Yaguang Zheng Youmin Cho Yun Jiang |
| author_sort | Misun Hwang |
| collection | DOAJ |
| description |
BackgroundArtificial 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.
ObjectiveThis 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.
MethodsA 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.
ResultsOf 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%).
ConclusionsA 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. |
| format | Article |
| id | doaj-art-44dbabc677da4a5ea80f18af59607052 |
| institution | DOAJ |
| issn | 1438-8871 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | Journal of Medical Internet Research |
| spelling | doaj-art-44dbabc677da4a5ea80f18af596070522025-08-20T03:17:04ZengJMIR PublicationsJournal of Medical Internet Research1438-88712025-04-0127e5963210.2196/59632AI Applications for Chronic Condition Self-Management: Scoping ReviewMisun Hwanghttps://orcid.org/0000-0001-8102-3308Yaguang Zhenghttps://orcid.org/0000-0002-8400-1398Youmin Chohttps://orcid.org/0000-0003-1381-652XYun Jianghttps://orcid.org/0000-0001-6737-5016 BackgroundArtificial 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. ObjectiveThis 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. MethodsA 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. ResultsOf 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%). ConclusionsA 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.https://www.jmir.org/2025/1/e59632 |
| spellingShingle | Misun Hwang Yaguang Zheng Youmin Cho Yun Jiang AI Applications for Chronic Condition Self-Management: Scoping Review Journal of Medical Internet Research |
| title | AI Applications for Chronic Condition Self-Management: Scoping Review |
| title_full | AI Applications for Chronic Condition Self-Management: Scoping Review |
| title_fullStr | AI Applications for Chronic Condition Self-Management: Scoping Review |
| title_full_unstemmed | AI Applications for Chronic Condition Self-Management: Scoping Review |
| title_short | AI Applications for Chronic Condition Self-Management: Scoping Review |
| title_sort | ai applications for chronic condition self management scoping review |
| url | https://www.jmir.org/2025/1/e59632 |
| work_keys_str_mv | AT misunhwang aiapplicationsforchronicconditionselfmanagementscopingreview AT yaguangzheng aiapplicationsforchronicconditionselfmanagementscopingreview AT youmincho aiapplicationsforchronicconditionselfmanagementscopingreview AT yunjiang aiapplicationsforchronicconditionselfmanagementscopingreview |