Digital twin prevalence in the medical caring fields: A bibliomatrics study and visualization analysis through CiteSpace

Abstract

Abstract Objectives: We conducted academic research utilizing the visualization tool CiteSpace to explore the direct relationship between digital twin technology and medical care. Methods: We collected data from the Web Of Science Core Collection, PubMed ScienceDirect, SpringerLink, and Wiley Online Library databases from 2010 to 2023, displayed visualization analysis of countries, institutions, and co-occurring keywords, clusters, citation bursts, and timelines, also calculated nodes, edges, centrality, modularity, and silhouette through CiteSpace 5.75r version. Results: The data incorporated 1109 studies, graphed the yearly publication number from 2010 to 2023, and showed a steady increase trend. The tree map displayed the top 10 prominent study subjects; the first one was “Health Care Science Service.” The top 3 countries were the United States, Germany, and England, and the top one institution was Harvard Medical School. The top 5 keywords were “digital health,” “care,” “technology,” “digital twin,” and “telemedicine.” The rank 3 clusters were “Digital Health Applications,” “Digital Twin,” and “Machine Learning.” We also displayed the visualization analysis of citation bursts and timelines. Conclusions: Digital twins have welcomed a popular development in strong countries and top-tier institutions and have a tight connection with mobile health and artificial intelligence. It has been widely used in clinical trials, like surgical operation and rehabilitation discipline, to predict patient treatment outcomes, and estimate potential complications; we should facilitate digital twins in clinical practice conversion and application and try to tackle the problems from privacy concerns and economic challenges.