Bibliometric Top Ten Healthcare-Related ChatGPT Publications in the First ChatGPT Anniversary

Abstract

Bibliometric analysis is a useful tool to assess influential publications on ChatGPT utility in healthcare, an emerging research topic. The aim of this study was to identify the top ten cited healthcare-related ChatGPT publications. The study employed an advanced search on three databases: Scopus, Web of Science, and Google Scholar to identify ChatGPT-related records in healthcare education, research, and practice by 30 November 2023. Ranking was based on the retrieved citation count in each database. The alternative metrics evaluated included PlumX metrics and Altmetric Attention Scores (AASs). A total of 22 unique records were identified in the three databases. Only two publications were found in the top 10 list across the three databases. The range of citation count varied per database with the highest range identified in Google Scholar (1019–121) followed by Scopus (242–88), and Web of Science (171–23). Google Scholar citations were correlated significantly with and the following metrics: Semantic Scholar highly influential citations (Spearman’s correlation coefficient (ρ) = .840, P < .001), PlumX captures (ρ = .831, P < .001), PlumX mentions (ρ = .609, P = .004), and AASs (ρ = .542, P = .009). Despite the several acknowledged limitations, bibliometric analysis in this study showed the evolving landscape of ChatGPT utility in healthcare. There is an urgent need for collaborative initiatives by all stakeholders involved to establish guidelines for ethical, transparent, and responsible use of ChatGPT in healthcare. The study revealed the correlation between citations and alternative metrics highlighting its usefulness as a supplement to gauge publication impact even in a rapidly growing research field.