Exploring the use of ChatGPT in OBGYN: a bibliometric analysis of the first ChatGPT-related publications

• Published in: Archives of gynecology and obstetrics Vol. 308; no. 6; pp. 1785 - 1789
• Main Authors: Levin, Gabriel, Brezinov, Yoav, Meyer, Raanan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2023; Springer Nature B.V
• Subjects: Artificial Intelligence; Bibliometrics; Chatbots; Databases, Factual; Endocrinology; General Gynecology; Gynecology; Human Genetics; Humans; Medicine; Medicine & Public Health; Obstetrics; Obstetrics/Perinatology/Midwifery; Research; OBGYN literature; Bibliometrics; ChatGPT; Artificial intelligence
• ISSN: 1432-0711; 0932-0067; 1432-0711
• DOI: 10.1007/s00404-023-07081-x

Purpose Little is known about the scientific literature regarding the new revolutionary tool, ChatGPT. We aim to perform a bibliometric analysis to identify ChatGPT-related publications in obstetrics and gynecology (OBGYN). Study design A bibliometric study through PubMed database. We mined all ChatGPT-related publications using the search term “ChatGPT”. Bibliometric data were obtained from the iCite database. We performed a descriptive analysis. We further compared IF among publications describing a study vs. other publications. Results Overall, 42 ChatGPT-related publications were published across 26 different journals during 69 days. Most publications were editorials (52%) and news/briefing (22%), with only one (2%) research article identified. Five (12%) publications described a study performed. No ChatGPT-related publications in OBGYN were found. The leading journal by the number of publications was Nature (24%), followed by Lancet Digital Health and Radiology (7%, for both). The main subjects of publications were ChatGPT’s scientific writing quality (26%) and a description of ChatGPT (26%) followed by tested performance of ChatGPT (14%), authorship and ethical issues (10% for both topics).In a comparison of publications describing a study performed ( n  = 5) vs. other publications ( n  = 37), mean IF was lower in the study-publications (mean 6.25 ± 0 vs. 25.4 ± 21.6, p  < .001). Conclusions The study highlights main trends in ChatGPT-related publications. OBGYN is yet to be represented in this literature.