The global burden of chronic diseases, characterized by their high prevalence, morbidity, mortality, and substantial socioeconomic impact, presents significant challenges to traditional healthcare models. These disorders, which typically persist for over three months and are generally incurable, account for nearly 74% of all global deaths annually, with direct medical costs in the United States alone representing 86% of total healthcare expenditures. Consequently, there is an urgent need for comprehensive and systemic measures to alleviate this escalating burden.
In response to this growing public health crisis, the integration of artificial intelligence (AI) into chronic disease management has emerged as an innovative solution. AI, defined as a field that simulates and extends human cognitive functions through computer technology to assist or augment human capabilities in complex tasks, has continuously expanded its application in healthcare since its introduction in 1956. In chronic disease management, AI enables comprehensive oversight from early screening to ongoing care, thereby enhancing the precision and efficiency of disease prevention and treatment. While research suggests that AI can improve patients’ quality of life and reduce strain on healthcare resources by optimizing health management strategies and improving chronic disease risk diagnosis, a systematic and comprehensive analysis of current research remains fragmented.
To address this gap, Pan M, Li R, Wei J, et al. conducted a bibliometric analysis focusing on the application of AI in chronic disease health management. This study aimed to identify research trends, highlight key areas, and provide valuable insights into the current state of the field, serving as a reference for guiding future research and fostering the effective application of AI in healthcare. The analysis, which retrieved 341 publications from the Web of Science Core Collection database up to August 2024, revealed a notable surge in publications between 2013 and 2024, accounting for 95.31% of the total output.
Key findings indicate that the United States is the leading contributor in this field, accounting for over 50% of global publications, underscoring its significant research capacity and influence. The Journal of Medical Internet Research was identified as the top journal by publication count, highlighting its prominence in the intersection of the internet and healthcare, particularly in AI and health management. The study also identified four primary research clusters: diagnosis, care, telemedicine, and technology. Recent trends emphasize mobile health technologies and machine learning as key focal points in AI applications for chronic disease management.
Despite significant advancements, challenges persist, including the need to improve research quality, foster greater international and inter-institutional collaboration, standardize data-sharing practices, and address ethical and legal concerns. While AI shows immense promise in areas like personalized treatment, continuous monitoring, and risk prediction for conditions such as diabetes, hypertension, and COPD, its applications in other chronic diseases like chronic kidney disease (CKD) and cardiovascular disease (CVD) are still limited. Future efforts should prioritize strengthening global partnerships, optimizing AI technologies for more precise and effective management, and ensuring seamless integration into clinical practice.
Reference for the Article:
Pan, M., Li, R., Wei, J., Peng, H., Hu, Z., Xiong, Y., Li, N., Guo, Y., Gu, W., & Liu, H. (2025). Application of artificial intelligence in the health management of chronic disease: bibliometric analysis. Frontiers in Medicine, 11, 1506641. https://doi.org/10.3389/fmed.2024.1506641
