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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
Bibliometric and visualized analysis of reporting and data systems from 2000 to 2022: research situation, global
Yan Wang1, Minghui Zhang1, Li Sang2
1Department of Ultrasonography, The First Affiliated Hospital of China Medical University, Shenyang, China.
Background:
The reporting and data system (RADS) has been researched across the world since it was first developed. This study used bibliometrics to analyze the research trends and current status of this field over the past almost 23 years and explored possible future research hotspots.
Methods:
We searched the Web of Science (WOS) literature on RADSs from January 1, 2000, to November 1, 2022, and evaluated the findings visually with VOSviewer (1.6.18), CiteSpace (6.1.3), and the "bibliometrix" package in R version 4.2.1.
Results:
We included 6,239 publications from 88 countries and regions. The number of published has shown an overall growth trend, especially since 2016. The United States was the country with the highest number of publications and citations. The top 10 most productive institutions in RADS research were mainly from South Korea and the United States. Kim EK was the most published author, and Turkbey B had the most cited publication. European Radiology had the most publications on the subject, while Radiology was the most influential journal. Magnetic resonance imaging, carcinoma, ultrasound, RADS, mammography, breast neoplasms, and diagnosis were the most common keywords. Artificial intelligence (AI) appears to be an emerging hotspot in the research of RADS.
Conclusions:
This study provides an overview of the development status of research into RADSs over the past 23 years. Research into RADSs has included various systems of the body, with the most studied being the breast, prostate, liver, and thyroid. In terms of auxiliary diagnosis, there is an increasing amount of research into the application of AI in RADSs, which along with the interpretability of AI, will be a hotspot of research in the following years.
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