ArticleJournal of hand and microsurgery2025
Gender and racial diversity Assumed by text-to-image generators in microsurgery and plastic surgery-related subspecialities.
Article in Journal of hand and microsurgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Accuracy of Descriptive and Illustrative Capacity of Generative Artificial Intelligence in Aesthetic Surgery: A Study of Accelerated Facial Aging in Depressed Medical Residents.Aesthetic plastic surgery · 2026Article
- Limitations of Artificial Intelligence Generated Images for Hand Surgery Patient Education.Journal of hand surgery global online · 2025Article
Corrections and comments
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Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Since the release of ChatGPT by OpenAI in November 2022, generative artificial intelligence (AI) models have attracted significant attention in various fields, including surgery. These advancements have been particularly notable for creating highly detailed and contextually accurate images from textual prompts. A notable area of clinical application is the representation of surgeon demographics in various specialties, particularly in the context of microsurgery and plastic surgery-related subspecialties. Methods: This cross-sectional study, conducted in June 2024, utilized the latest version of the Copilot Creative Mode powered by DALL-E 3 to generate images of surgeons across various plastic surgery subspecialties. Real-world demographic data from the US, Japan, and Zambia were compared with AI-generated images for an accurate representation analysis. Results: Five hundred images (350 from various subspecialties and 150 from geographical sources) were analyzed. The AI model predominantly generated images of male and female surgeons with a statistical underrepresentation of female and Black microsurgeons. Geographical prompts influenced the representation, with an overrepresentation of female (64.0 %; p < 0.001) and Black (16.0 %; p < 0.001) plastic surgeons in the US and exclusively Asian surgeons in Japan. Discrepancies were also observed in the depiction of surgical equipment, with the majority of AI-generated microsurgeons inaccurately portrayed using either surgical loupes (46.0 %) or optical microscopes (32.0 %), not with surgical microscopes (4.0 %). Conclusions: This study revealed significant disparities between AI-generated images and actual demographics in the fields of microsurgery and plastic surgery-related subspecialties, highlighting the need for more diverse and accurate training datasets for AI models.
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Registered trials
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