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Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR
BACKGROUND: Artificial intelligence (AI) has had a significant impact on our lives and plays many roles in various fields. By analyzing the past 30 years of AI trends in the field of nephrology, using a bibliography, we wanted to know the areas of interest and future direction of AI in research rela...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036048/ https://www.ncbi.nlm.nih.gov/pubmed/33832141 http://dx.doi.org/10.1097/MD.0000000000025422 |
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author | Park, Sihyung Park, Bong Soo Lee, Yoo Jin Kim, Il Hwan Park, Jin Han Ko, Junghae Kim, Yang Wook Park, Kang Min |
author_facet | Park, Sihyung Park, Bong Soo Lee, Yoo Jin Kim, Il Hwan Park, Jin Han Ko, Junghae Kim, Yang Wook Park, Kang Min |
author_sort | Park, Sihyung |
collection | PubMed |
description | BACKGROUND: Artificial intelligence (AI) has had a significant impact on our lives and plays many roles in various fields. By analyzing the past 30 years of AI trends in the field of nephrology, using a bibliography, we wanted to know the areas of interest and future direction of AI in research related to the kidney. METHODS: Using the Institute for Scientific Information Web of Knowledge database, we searched for articles published from 1990 to 2019 in January 2020 using the keywords AI; deep learning; machine learning; and kidney (or renal). The selected articles were reviewed manually at the points of citation analysis. RESULTS: From 218 related articles, we selected the top fifty with 1188 citations in total. The most-cited article was cited 84 times and the least-cited one was cited 12 times. These articles were published in 40 journals. Expert Systems with Applications (three articles) and Kidney International (three articles) were the most cited journals. Forty articles were published in the 2010s, and seven articles were published in the 2000s. The top-fifty most cited articles originated from 17 countries; the USA contributed 16 articles, followed by Turkey with four articles. The main topics in the top fifty consisted of tumors (11), acute kidney injury (10), dialysis-related (5), kidney-transplant related (4), nephrotoxicity (4), glomerular disease (4), chronic kidney disease (3), polycystic kidney disease (2), kidney stone (2), kidney image (2), renal pathology (2), and glomerular filtration rate measure (1). CONCLUSIONS: After 2010, the interest in AI and its achievements increased enormously. To date, AIs have been investigated using data that are relatively easy to access, for example, radiologic images and laboratory results in the fields of tumor and acute kidney injury. In the near future, a deeper and wider range of information, such as genetic and personalized database, will help enrich nephrology fields with AI technology. |
format | Online Article Text |
id | pubmed-8036048 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-80360482021-04-13 Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR Park, Sihyung Park, Bong Soo Lee, Yoo Jin Kim, Il Hwan Park, Jin Han Ko, Junghae Kim, Yang Wook Park, Kang Min Medicine (Baltimore) 5200 BACKGROUND: Artificial intelligence (AI) has had a significant impact on our lives and plays many roles in various fields. By analyzing the past 30 years of AI trends in the field of nephrology, using a bibliography, we wanted to know the areas of interest and future direction of AI in research related to the kidney. METHODS: Using the Institute for Scientific Information Web of Knowledge database, we searched for articles published from 1990 to 2019 in January 2020 using the keywords AI; deep learning; machine learning; and kidney (or renal). The selected articles were reviewed manually at the points of citation analysis. RESULTS: From 218 related articles, we selected the top fifty with 1188 citations in total. The most-cited article was cited 84 times and the least-cited one was cited 12 times. These articles were published in 40 journals. Expert Systems with Applications (three articles) and Kidney International (three articles) were the most cited journals. Forty articles were published in the 2010s, and seven articles were published in the 2000s. The top-fifty most cited articles originated from 17 countries; the USA contributed 16 articles, followed by Turkey with four articles. The main topics in the top fifty consisted of tumors (11), acute kidney injury (10), dialysis-related (5), kidney-transplant related (4), nephrotoxicity (4), glomerular disease (4), chronic kidney disease (3), polycystic kidney disease (2), kidney stone (2), kidney image (2), renal pathology (2), and glomerular filtration rate measure (1). CONCLUSIONS: After 2010, the interest in AI and its achievements increased enormously. To date, AIs have been investigated using data that are relatively easy to access, for example, radiologic images and laboratory results in the fields of tumor and acute kidney injury. In the near future, a deeper and wider range of information, such as genetic and personalized database, will help enrich nephrology fields with AI technology. Lippincott Williams & Wilkins 2021-04-09 /pmc/articles/PMC8036048/ /pubmed/33832141 http://dx.doi.org/10.1097/MD.0000000000025422 Text en Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) |
spellingShingle | 5200 Park, Sihyung Park, Bong Soo Lee, Yoo Jin Kim, Il Hwan Park, Jin Han Ko, Junghae Kim, Yang Wook Park, Kang Min Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR |
title | Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR |
title_full | Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR |
title_fullStr | Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR |
title_full_unstemmed | Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR |
title_short | Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR |
title_sort | artificial intelligence with kidney disease: a scoping review with bibliometric analysis, prisma-scr |
topic | 5200 |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036048/ https://www.ncbi.nlm.nih.gov/pubmed/33832141 http://dx.doi.org/10.1097/MD.0000000000025422 |
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