Evolution of artificial intelligence in medical sciences: a comprehensive scientometrics analysis

Abstract

Purpose The purpose of this study is to analyze the trend of scientific publications, geographic and organizational distribution, and examine the keyword cooccurrence map in the field of artificial intelligence (AI) in medical sciences. Design/methodology/approach The applied research has used the scientometrics method to analyze data to AI in medical sciences. The data were extracted from the WOSCC database. Data analysis was performed using the bibliometrix software. Findings According to the results, 41,352 scientific documents in the field of AI in medical sciences were extracted, the growth trend of which has increased significantly since 2000. The USA, China and England were identified as leaders in this field, and universities, such as Harvard University and the University of California, contributed the most to related knowledge production. Moreover, the terms “machine learning” and “deep learning” have been proposed as key concepts in this field. Practical implications The findings of this study highlight the significant role of AI in advancing medical research and healthcare systems. By fostering international collaboration and focusing on emerging trends, the integration of AI can lead to improved healthcare outcomes and the development of innovative solutions that address pressing medical challenges. Originality/value This research contributes to the existing body of knowledge by providing a comprehensive analysis of the trends, geographic distribution and key concepts associated with AI in medical sciences. By using scientometric methods and bibliometrix software, this study offers a unique perspective on the evolution of AI research within the medical field, identifying leading institutions and pivotal concepts such as “machine learning” and “deep learning.”

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