Affiliation:
1. Department of Healthcare Information and Management, School of Health and Medical Engineering, Ming Chuan University, Taipei 111, Taiwan
2. Outcomes and Translational Sciences, College of Pharmacy, The Ohio State University, Columbus, OH 43210, USA
3. Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei 110, Taiwan
Abstract
Artificial intelligence (AI) has emerged as a promising tool in the field of healthcare, with an increasing number of research articles evaluating its applications in the domain of kidney disease. To comprehend the evolving landscape of AI research in kidney disease, a bibliometric analysis is essential. The purposes of this study are to systematically analyze and quantify the scientific output, research trends, and collaborative networks in the application of AI to kidney disease. This study collected AI-related articles published between 2012 and 20 November 2023 from the Web of Science. Descriptive analyses of research trends in the application of AI in kidney disease were used to determine the growth rate of publications by authors, journals, institutions, and countries. Visualization network maps of country collaborations and author-provided keyword co-occurrences were generated to show the hotspots and research trends in AI research on kidney disease. The initial search yielded 673 articles, of which 631 were included in the analyses. Our findings reveal a noteworthy exponential growth trend in the annual publications of AI applications in kidney disease. Nephrology Dialysis Transplantation emerged as the leading publisher, accounting for 4.12% (26 out of 631 papers), followed by the American Journal of Transplantation at 3.01% (19/631) and Scientific Reports at 2.69% (17/631). The primary contributors were predominantly from the United States (n = 164, 25.99%), followed by China (n = 156, 24.72%) and India (n = 62, 9.83%). In terms of institutions, Mayo Clinic led with 27 contributions (4.27%), while Harvard University (n = 19, 3.01%) and Sun Yat-Sen University (n = 16, 2.53%) secured the second and third positions, respectively. This study summarized AI research trends in the field of kidney disease through statistical analysis and network visualization. The findings show that the field of AI in kidney disease is dynamic and rapidly progressing and provides valuable information for recognizing emerging patterns, technological shifts, and interdisciplinary collaborations that contribute to the advancement of knowledge in this critical domain.
Reference72 articles.
1. Prevalence and disease burden of chronic kidney disease;Lv;Adv. Exp. Med. Biol.,2019
2. Han, C.T., Islam, M.M., Poly, T.N., Lu, Y.-C., and Lin, M.-C. (2023). A Meta-Analysis of Proton Pump Inhibitor Use and the Risk of Acute Kidney Injury: Geographical Differences and Associated Factors. J. Clin. Med., 12.
3. Sustainable development goals relevant to kidney health: An update on progress;Luyckx;Nat. Rev. Nephrol.,2021
4. The Growing Challenge of Chronic Kidney Disease: An Overview of Current Knowledge;Borg;Int. J. Nephrol.,2023
5. Wang, V., Vilme, H., Maciejewski, M.L., and Boulware, L.E. (2016). Seminars in Nephrology, W. B. Saunders.