ArticleAnnals of medicine and surgery (2012)2026
Cross-race vs. same-race collaboration in plastic surgery: AI-driven analysis of research and race-interaction dynamics.
Article in Annals of medicine and surgery (2012), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
Funding
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Abstract
Background: Racial diversity in plastic surgery research remains a challenge. This study employs artificial intelligence (AI)-driven big data analysis to assess racial representation, cross-race mentorship trends, and research productivity disparities among plastic surgery authors over 12 years (2010-2022). Methods: A retrospective, cross-sectional study was conducted using AI-based web scraping to extract authorship data from 24 PubMed-indexed plastic surgery journals. RaceAPI, a machine-learning-based name-classification tool trained on U.S. Census data, inferred racial identity. Chi-square tests were used to evaluate racial disparities in first and senior authorship, mentorship patterns, and research output. Results: Among 16 019 publications, White researchers remained the dominant group among first authors (70% in 2010 vs. 66.2% in 2022, Conclusions: Persistent racial disparities in authorship and research participation among racial groups that are persistently underrepresented in medical school and plastic surgery residencies suggest the need to assess and address educational barriers prior to medical school. Strengthening mentorship networks and increasing access to research for minority trainees could foster a thriving plastic surgery workforce for all communities, including those currently underserved. Level of Evidence: Level III, cross-sectional study.
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