Abstract
This study provides a bibliometric review of deep learning applications in diabetes between 2018 and 2022, with an analysis of the 2201 publications. This review highlights the influential aspects of deep learning in diabetes research from a bibliometric perspective. Deep learning has drawn significant interest from researchers, particularly those working in diabetes. Two well-known databases: Web of Science and Scopus, each of which having its own data format, are combined into a single format using the R programming language in R Studio, and the duplicates are removed. The Bibliometrix package is used to conduct quantitative analysis, which includes highlighting the primary journals, the works that have been referenced the most, the authors, nations, and institutions that have produced the most, as well as keyword clustering, paper split into sub-periods to track theme progression, and top trend analysis. The findings demonstrate a notable increase in publications since 2018. A plethora of studies are conducted on the practical applications of deep learning to treat diabetes, which is dramatically rising. IEEE Access, Scientific Reports, and Computers in Biology and Medicine are the top three most relevant journals. China is most productive and its publications are highly cited, while the USA comes second. Accuracy, atrial fibrillation, and heart infarction have recently been the hot topics. The most frequently used words are human, article, and diabetes mellitus. The findings help academics better understand the study area in this related field, which is one of the hottest research fields in Artificial Intelligence.
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