Artificial intelligence and Machine Learning Trends in Kidney Care.
Artificial intelligence
Bibliometric
Citation analysis
Kidney Care
Machine learning
Nephrology
Publication trends
SCI-EXPANDED
Journal
The American journal of the medical sciences
ISSN: 1538-2990
Titre abrégé: Am J Med Sci
Pays: United States
ID NLM: 0370506
Informations de publication
Date de publication:
26 Jan 2024
26 Jan 2024
Historique:
received:
05
05
2023
revised:
12
12
2023
accepted:
23
01
2024
medline:
29
1
2024
pubmed:
29
1
2024
entrez:
28
1
2024
Statut:
aheadofprint
Résumé
The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics. The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5,425 documents were identified and analyzed. The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology. The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.
Sections du résumé
BACKGROUND
BACKGROUND
The integration of artificial intelligence (AI) and machine learning (ML) in kidney care has seen a significant rise in recent years. This study specifically analyzed AI and ML research publications related to kidney care to identify leading authors, institutions, and countries in this area. It aimed to examine publication trends and patterns, and to explore the impact of collaborative efforts on citation metrics.
METHODS
METHODS
The study used the Science Citation Index Expanded (SCI-EXPANDED) of Clarivate Analytics Web of Science Core Collection to search for AI and machine learning publications related to nephrology from 1992 to 2021. The authors used quotation marks and Boolean operator "or" to search for keywords in the title, abstract, author keywords, and Keywords Plus. In addition, the 'front page' filter was applied. A total of 5,425 documents were identified and analyzed.
RESULTS
RESULTS
The results showed that articles represent 75% of the analyzed documents, with an average author to publications ratio of 7.4 and an average number of citations per publication in 2021 of 18. English articles had a higher citation rate than non-English articles. The USA dominated in all publication indicators, followed by China. Notably, the research also showed that collaborative efforts tend to result in higher citation rates. A significant portion of the publications were found in urology journals, emphasizing the broader scope of kidney care beyond traditional nephrology.
CONCLUSION
CONCLUSIONS
The findings underscore the importance of AI and ML in enhancing kidney care, offering a roadmap for future research and implementation in this expanding field.
Identifiants
pubmed: 38281623
pii: S0002-9629(24)00051-X
doi: 10.1016/j.amjms.2024.01.018
pii:
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Informations de copyright
Copyright © 2024. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of competing Interest The authors alone are responsible for the content and writing of the paper. This paper has not received any financial support, endorsement, or oversight from any commercial entity. Drs. Fülöp and Soliman is current employee of the United States Veterans Health Administration. However, the opinions and views expressed in this paper are the Authors’ own and do not represent the official views or policies of the United States Veteran Health Administrations.