ArticleThe Journal of international medical research2026
Visual representation bias in artificial intelligence-generated depictions of anesthesia, pain, and intensive care professionals.
Article in The Journal of international medical research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ObjectiveTo evaluate the demographic and hierarchical representation biases in artificial intelligence-generated depictions of healthcare professionals in anesthesiology, pain medicine, and intensive care, with a focus on sex, age group, skin tone, ethnicity, and professional role hierarchy.MethodsThis cross-sectional comparative study analyzed 4400 artificial intelligence-generated images produced by 4 text-to-image models (DALL·E 3, Midjourney, Leonardo AI, and Gemini). Ten professional roles in anesthesiology, pain medicine, and intensive care, ranging from trainees to department heads, were evaluated. Two independent anesthesiologists with clinical experience in anesthesiology and intensive care assessed each image using a structured digital evaluation form, recording sex, age group, skin tone, ethnicity, and professional role hierarchy. Statistical comparisons were performed using chi-square test with Bonferroni-adjusted post hoc proportion analyses.ResultsMale representation predominated across roles and models (mean: 68.5%), with leadership positions showing the highest male proportion (up to 90.0%). Light skin tones (mean: 70.6%) and Caucasian ethnicity (mean: 68.0%) were the most commonly depicted categories. Younger individuals (age: <40 years) were overrepresented (mean: 53.1%), whereas individuals aged >60 years were rarely depicted (<3.0%) and appeared mainly in leadership roles. Significant differences in sex representation were observed in 9 out of 10 professional roles across artificial intelligence models, with the exception of the anesthesia specialist role (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.