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Gender and racial diversity Assumed by text-to-image generators in microsurgery and plastic surgery-related
Makoto Shiraishi1, Chihena Hansini Banda2, Mayuri Nakajima1
1Department of Plastic and Reconstructive Surgery, The University of Tokyo Hospital, Tokyo, Japan.
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.
