Knowledge Graph of Artificial Intelligence in Medicine: A Scientometric Analysis

Abstract

Background Artificial intelligence (AI) is evolving rapidly and gradually changing the landscape of healthcare and biomedicine. AI has achieved breakthroughs in image-based diagnosis, interpretation of electronic medical records, etc. However, no systematic quantitative analysis has been conducted to provide deeper insights of the status and frontier trends of AI in medicine (AI-MED).Methods We employed a scientometric and visualization approach to analyze the annual publications, countries, journals, keywords, co-citations, and structural variability to establish a knowledge graph that summarizes the hotspots and trends of AI-MED with a quantitative method.Findings There were 30,458 publications screened from the Web of Science (WOS). The number of publications has been growing rapidly. The most prolific countries are the USA and China. Artificial neural networks, machine learning, deep learning, convolutional neural network, image segmentation, and COVID-19 are hotspots in AI-MED.Conclusions This study has made clear the research process, frontier trends and emerging fields of AI-MED, predicting its future and pointing out the path for researchers to grasp the hotspots and directions in AI-MED quickly.

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