Global Research Trends of Gender-Related Artificial Intelligence in Medicine Between 2001–2020: A Bibliometric Study

Ha Young Yoon, Heisook Lee, Jeong Yee, Hye Sun Gwak

Research output: Contribution to journalArticlepeer-review

Abstract

This study aimed to assess the research on medical Artificial intelligence (AI) related to sex/gender and explore global research trends over the past 20 years. We searched the Web of Science (WoS) for gender-related medical AI publications from 2001 to 2020. We extracted the bibliometric data and calculated the annual growth of publications, Specialization Index, and Category Normalized Citation Impact. We also analyzed the publication distributions by institution, author, WoS subject category, and journal. A total of 3,110 papers were included in the bibliometric analysis. The number of publications continuously increased over time, with a steep increase between 2016 and 2020. The United States of America and Harvard University were the country and institution that had the largest number of publications. Surgery and urology nephrology were the most common subject categories of WoS. The most occurred keywords were machine learning, classification, risk, outcomes, diagnosis, and surgery. Despite increased interest, gender-related research is still low in medical AI field and further research is needed.

Original languageEnglish
Article number868040
JournalFrontiers in Medicine
Volume9
DOIs
StatePublished - 17 May 2022

Keywords

  • artificial intelligence
  • bibliometric analysis
  • gender
  • medical research
  • medicine

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