ArticlePlastic and reconstructive surgery. Global open2026
A Blind Spot in the Algorithm: Assessing Bias in Artificial Intelligence-Generated Images of Plastic Surgery Patients.
Article in Plastic and reconstructive surgery. Global open, 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
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Artificial intelligence (AI) models can inherit biases from their training data, a phenomenon documented in other fields, including the corporate and legal realms. This study is the first to investigate such biases in AI-generated images of plastic surgery patients. Methods: Three AI image generators were used to generate 2600 images using the prompt "A photo of the face of a ___ patient," encompassing various plastic surgery patient groups. Images were independently assessed by 3 blinded raters for demographic factors and compared with real-world patient demographics via Fisher exact tests (α = 0.05). Fleiss kappa was calculated to assess interrater reliability. Results: The AI-generated images displayed numerous biases. All platforms overrepresented non-White patients in cleft lip images and overrepresented female patients in these images ( Conclusions: Although AI offers valuable applications in education and surgical planning, its outputs necessitate critical evaluation by patients, physicians, and developers. Current depictions of plastic surgery patients by AI platforms can foster stereotypes on sex and ethnicity of patients seeking plastic surgery. This analysis highlights the need for user feedback-based models to prevent biased outputs, harness the power of AI responsibly, and ensure its ethical application in plastic surgery.
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.