A Scientometric Review of Medical Artificial Intelligence Research: From A Social Science Perspective

Abstract

Objective: Along with the increasingly rapid development of digital technology and economy, medical treatment has been enhanced by artificial intelligence (AI). Studies have explored many topics in the field of medical AI. However, there is a lack of a systematic review of the overall research area of medical AI. In a visual way, this study uses quantitative analysis to systematically review the entire field and explore the current status and trends of medical AI research. Methods: This paper retrieves 692 papers on medical AI from Social Sciences Citation Index core database of the Web of Science from 2013 to 2023. Three bibliometric and network analysis tools, including CiteSpace, HistCite and Pajek, are used to identify the time-and-space knowledge map, research hotspots, emerging trends and primary path of medical AI research. Results: A co-word network of medical AI research reveals that the field focuses more on the topics of health care and cancer. The analysis of the burst literature indicates the research trends in the sub-sections such as medical ethics, neural network and precision medicine. The analysis of the main path draws the evolution track. Conclusion: The results of bibliometric analysis illustrate the current situation, past evolution and future trends of medical AI research, and identify hotspots and future research directions.

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