Du Chao, Zhou Jing, Yu Yuexin
Department of Reproductive Medicine, General Hospital of Northern Theater Command, Shenyang, China.
Front Digit Health. 2025 Jun 18;7:1608266. doi: 10.3389/fdgth.2025.1608266. eCollection 2025.
The incidence of chronic diseases is increasing annually and exhibits a trend of multimorbidity, posing significant challenges to global healthcare and nursing. The rapid rise of artificial intelligence has provided broad application prospects in the field of chronic disease care. However, with the increasing number of related studies, there is a lack of systematic review and prediction of future trends in this area. Bibliometric methods provide possibility for addressing this gap. This study aimed to investigate the current status, hot topics, and future prospects of artificial intelligence in the field of chronic disease care.
Literature related to artificial intelligence and chronic disease care was retrieved from the Web of Science Core Collection database, published between 2001 and 31 December 2023. Bibliometric analysis and visualization was conducted using CiteSpace 5.7.R5 and VOSviewer 1.6.19 to analyze countries/regions, institutions, journals, references, and keywords.
A total of 2438 articles were retrieved, indicating an explosive growth in publications over the past five years. The United States emerged as the earliest adopter of research in this domain (since 2002) and contributed the most publications (490 articles), with IEEE ACCESS being the most cited journal. Hot application areas of artificial intelligence in chronic disease care included "diabetic retinopathy", "heart disease prediction", "breast cancer", and "skin cancer". Major research methodologies encompassed "machine learning", "deep learning", "neural network", and "text mining". Potential future research hotspots include "internet of medical things".
This study unveils the current status and development trends of artificial intelligence in chronic disease care, offering novel insights for future artificial intelligence application research.
慢性病的发病率逐年上升，呈现出多种疾病并存的趋势，给全球医疗保健和护理带来了重大挑战。人工智能的迅速崛起为慢性病护理领域提供了广阔的应用前景。然而，随着相关研究数量的增加，该领域缺乏系统的综述和对未来趋势的预测。文献计量学方法为弥补这一差距提供了可能。本研究旨在探讨人工智能在慢性病护理领域的现状、热点话题和未来前景。
从科学网核心合集数据库中检索2001年至2023年12月31日发表的与人工智能和慢性病护理相关的文献。使用CiteSpace 5.7.R5和VOSviewer 1.6.19进行文献计量分析和可视化，以分析国家/地区、机构、期刊、参考文献和关键词。
共检索到2438篇文章，表明过去五年出版物呈爆发式增长。美国是该领域最早开展研究的国家（自2002年起），发表的文章数量最多（490篇），被引用次数最多的期刊是《IEEE接入》。人工智能在慢性病护理中的热门应用领域包括“糖尿病视网膜病变”、“心脏病预测”、“乳腺癌”和“皮肤癌”。主要研究方法包括“机器学习”、“深度学习”、“神经网络”和“文本挖掘”。未来潜在的研究热点包括“医疗物联网”。
本研究揭示了人工智能在慢性病护理中的现状和发展趋势，为未来人工智能应用研究提供了新的见解。
