A data-driven analysis of global research trends in medical image: A survey

Abstract

With the development of artificial intelligence and high-performance computing equipment, new technologies have huge effects on medical image research. However, it is difficult to find out when new research topics appear, who those authors with influences are, and how relevant publications influence the academic community. In order to catch up with global research trends, traditional methods of literature review are inadequate to acquire information. In this case, a data-driven analysis offers a new quantitative approach to studying global research trends. Specifically, this paper used several basic bibliometric indexes to characterize the global trends of medical image research from 1993 to 2022, including yearly output, active journals, important authors, active institutions, and main countries. Furthermore, we utilized network-based methods to analyze the internal relations of co-word, co-authorship, and co-citation, so as to discover academic hotspots and clarify global research trends. Finally, some conclusions are drawn as follow: (1) The present medical image research is on an upward trend. The number of publications on medical image surged since 2015 due to advances in deep learning. Deep learning and convolutional neural networks (CNNs) are both popular research keywords in recent years. (2) IEEE Transactions on Medical Imaging is the most influential journal in view of Total Local Citation Score (TLCS) and Total Global Citation Score (TGCS), followed by Medical Image Analysis. Neurocomputing and Information Fusion are well-recognized in local research community. (3) Van Ginneken B and Aerts HJWL are representative scholars in consideration of TLCS and TGCS. (4) The USA is a leading country in medical image research. Other influential countries include China, India, UK, Germany, France, Canada, Netherlands, Australia, Italy, South Korea, Switzerland, etc. Most important institutions are from these countries, including Harvard, UMich, Stanford, UPenn, UNC, CAS, SJTU, UCL, UofT, RU, etc. (5) Application of artificial intelligence technologies, especially CNNs, has dramatically promoted global studies of medical image since 2015. Interdisciplinary collaborations become popular among experts with different disciplines backgrounds. We can infer that medical image analysis and application based on deep learning will still be a flourishing field in the near future with the improvement of algorithms and the application of high-performance computing equipment.