Artificial intelligence with kidney disease: A scoping review with bibliometric analysis, PRISMA-ScR. Issue 14 (9th April 2021)

• Record Type: Journal Article
• Authors: Park, Sihyung; Park, Bong Soo; Lee, Yoo Jin; Kim, Il Hwan; Park, Jin Han; Ko, Junghae; Kim, Yang Wook; Park, Kang Min
• Other Names: Mallik. Leena section editor.
• Is Part Of: Medicine. Volume 100:Issue 14(2021)
• Publication Date: 2021-04-09
• Subjects: artificial intelligence -- bibliography -- kidney -- nephrology; Medicine -- Periodicals; Medicine -- Periodicals; Médecine -- Périodiques; Geneeskunde; Medicine; Periodicals; Periodicals; 610.5
• DOI: 10.1097/MD.0000000000025422
• Languages: English
• ISSNs: 0025-7974

Abstract

Abstract: 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.