A Blind Spot in the Algorithm: Assessing Bias in Artificial Intelligence–Generated Images of Plastic Surgery Patients

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 ( P < 0.001). Platforms exclusively depicted female patients in aesthetic surgery images involving facial cosmetic surgery and breast augmentation. Non-White patients and those older than 50 years were underrepresented in images related to aesthetic surgery ( P < 0.0001). Non-White patients were also underrepresented in images of breast augmentation and breast reconstruction ( P < 0.0001). 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.

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