Direction of Machine Learning Research in Obstetric Care in the Last 10 Years

Abstract

This study aims to use bibliometric analysis to examine how machine learning research trends in obstetric care have evolved over time. First, 829 publications from 2014 to 2023 were collected from the Web of Science database. The fundamental issues and characteristics of machine learning in obstetric care research were analyzed, such as annual publication contribution and focus, using theme analysis, concurrency analysis, and topic trend analysis. Second, productive objects, such as journals, authors, institutions, countries, regions, and Collaborative relationship mapping, are used to present who is leading the considerable attention in machine learning research in obstetric care. Third, the citation structure of authors and journals was investigated, and references were cited. This Bibliometric analysis revealed an increasing annual publication trend, a shift in focus to machine learning in obstetric care, the dominance of authors from China, and an increasing number of international collaborations, indicating machine learning in obstetric care as a vibrant and growing field and need for further scientific enrichment. Therefore, a comprehensive study on machine learning in obstetric care reviews the characteristics and trajectory of current research and helps scholars find suitable research entry points and conduct in-depth research.

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