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Beyond the prompt: gender ratio in text-to-image models, with a case study on hospital professions | Synapse
March 3, 2026
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Beyond the prompt: gender ratio in text-to-image models, with a case study on hospital professions
FV
Franck Vandewiele
Université du littoral côte d'opale
RS
Rémi Synave
Université du littoral côte d'opale
SD
Samuel Delepoulle
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Puntos clave
Identified significant gender ratio bias in text-to-image models, indicating underrepresentation of females.
The analysis revealed that only 30% of generated images for nursing roles depicted women, showing a clear disparity.
This observational analysis assessed several text-to-image models specifically focused on healthcare settings.
Findings suggest a need for improved fairness in algorithmic representations of hospital professions.
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Cite This Study
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Vandewiele et al. (Sun,) studied this question.
synapsesocial.com/papers/69a76601badf0bb9e87db457
https://doi.org/https://doi.org/10.1007/s43681-026-00997-5