Geographic and gender diversity in dental artificial-intelligence research authorship: a bibliometric analysis

Zuzel Salazar Duany, Tamara Torres Fernández, Yoandra Muro Valle, Jaquelin Virginia Junco Arevalo and Jhossmar Cristians Auza-Santivañez

SAP Artificial Intelligence in Dentistry, 2025

Abstract: Introduction: Artificial intelligence is reshaping dentistry, but who produces that research matters for whose needs are served. We quantified the geographic, income-group and gender distribution of dental AI authorship and its temporal trends. Methods: Dental AI articles published 2013-2025 were retrieved from OpenAlex, and author gender was inferred from first names with gender-guesser, with an unknown category. Countries were mapped to World Bank income groups and regions. Distributions were computed for all, first and last authorship, and international collaboration, with trends across three periods. Results: The corpus comprised 12,674 articles and 64,763 authorships from 150 countries. Among authors with inferable gender, 64.24% were male and 35.76% female, and the female share was lower in last authorship (35.76%) than first authorship (35.76%). Authorship was concentrated in high-income (56.51%) and upper-middle-income (33.67%) countries, with lower-middle-income countries contributing 9.82%, although that share rose from 2.01% in 2013-2016 to 10.30% in 2021-2025. Europe and East Asia and the Pacific contributed most authorships, and 34.20% of articles were internationally collaborative. Conclusions: Dental AI authorship is male-dominated, especially in senior positions, and geographically concentrated in high-income countries, with a slowly rising but still small lower-middle-income share. These patterns should inform efforts to broaden who builds dental AI.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:cwf:aidart:aid202547

DOI: 10.62486/aid202547

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