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Research trends in the application of artificial intelligence in nursing of chronic disease: a bibliometric and network visualization study.

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Název: Research trends in the application of artificial intelligence in nursing of chronic disease: a bibliometric and network visualization study.
Autoři: Du, Chao, Zhou, Jing, Yu, Yuexin
Zdroj: Frontiers in Digital Health; 2025, p1-10, 10p
Témata: SERIAL publications, HEART diseases, SKIN tumors, DATA mining, ARTIFICIAL intelligence, DIABETIC retinopathy, BREAST tumors, CONVOLUTIONAL neural networks, CITATION analysis, CHRONIC diseases, BIBLIOGRAPHICAL citations, BIBLIOMETRICS, MEDICAL research, DEEP learning, BIBLIOGRAPHY, MACHINE learning, AUTHORS, INTERNET of things
Geografický termín: UNITED States
Abstrakt: Purpose: 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. Methods: 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. Results: 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". Conclusion: 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. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
Popis
Abstrakt:Purpose: 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. Methods: 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. Results: 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". Conclusion: 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. [ABSTRACT FROM AUTHOR]
ISSN:2673253X
DOI:10.3389/fdgth.2025.1608266