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

Background: 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. Methods: 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 Englishlanguage 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. Results: 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 intelligence (AI), deep learning, Internet of Things (IoT) technologies, genomic data analysis, and bioinformatics. Case studies show how big data and medical device integration, exemplified by Electronic Health Records (EHRs), Clinical Decision Support Systems (CDSS), and smart wearables, enhance diagnostic efficiency and care quality. The study further identified intensive care units, radiology departments, and oncology as key application departments leveraging this integration. Conclusion: Big data and medical device integration have led to smart healthcare and personalized treatment, improving medical decision accuracy and inspiring new research directions. Future research efforts should explore the potential for incorporating even more expansive datasets and multilingual resources to further maximize the potential of this integration and advance medical technology in disease prevention, diagnosis, treatment, and management.