Global quantitative analysis and visualization of big data and medical devices based on bibliometrics

作者：Xiaoyang Bai, Jiajia Duan, Bo Li, Shuaiqiang Fu, Wenjie Yin, Zhenwei Yang, Zhifeng Qu · 发表于：Expert Systems with Applications · 年份：2024 · DOI：10.1016/j.eswa.2024.124398 · 被引用次数：14 · 研究领域：Artificial Intelligence in Healthcare and Education、Artificial Intelligence in Healthcare

In the big data era, the healthcare sector grapples with increased data volumes and the push for more intelligent medical devices. This challenge is marked by data silos, higher data processing needs, and the quest for personalized medicine, emphasizing the importance of integrating and analyzing diverse big data from medical devices. This study employs bibliometric analysis to thoroughly analyze the research trends in big data and medical device research, aiming to identify key developments, patterns, and potential future directions in the field. This study employed the Web of Science Core Collection, BIOSIS Citation Index, and Derwent Innovations Index for conducting keyword searches on ’big data’ and ’medical devices’, focusing on English-language articles, reviews, and patents, while excluding duplicates. Quantitative analyses and visualizations were facilitated using tools such as R 4.3.1, VOSviewer, CiteSpace, and Tableau. The research assessed the impact of journals and academic contributions through metrics like the impact factor and G-Index. Our analysis covered 592 articles and 795 patents. Notably, the annual growth rate of articles reached 62.19%, with the primary contributions originating from China, the United States, India, and England. Among the identified publications, “IEEE Access” emerged as the most prominent journal. The research identified key trends in the application of big data within medical devices, including the extensive use of artificial intellig...