Artificial Intelligence Portrayals in Orthopaedic Surgery

Abstract

Background: The increasing accessibility of artificial intelligence (AI) text-to-image generators offers a novel avenue for exploring societal perceptions. The present study assessed AI-generated images to examine the representation of gender and racial diversity among orthopaedic surgeons. Methods: Five prominent text-to-image generators (DALL·E 2, Runway, Midjourney, ImagineAI, and JasperArt) were utilized to create images for the search queries “Orthopedic Surgeon,” “Orthopedic Surgeon’s Face,” and “Portrait of an Orthopedic Surgeon.” Each query produced 80 images, resulting in a total of 240 images per generator. Two independent reviewers categorized race, sex, and age in each image, with a third reviewer resolving discrepancies. Images with incomplete or multiple faces were excluded. The demographic proportions (sex, race, and age) of the AI-generated images were then compared with those of the 2018 American Academy of Orthopaedic Surgeons (AAOS) census. Results: In our examination across all AI platforms, 82.8% of the images depicted surgeons as White, 12.3% as Asian, 4.1% as Black, and 0.75% as other; 94.5% of images were men; and a majority (64.4%) appeared ≥50 years old. DALL·E 2 exhibited significantly increased diversity in representation of both women and non-White surgeons compared with the AAOS census, whereas Midjourney, Runway, and ImagineAI exhibited significantly decreased representation. Conclusions: The present study highlighted distortions in AI portrayal of orthopaedic surgeon diversity, influencing public perceptions and potentially reinforcing disparities. DALL·E 2 and JasperArt show encouraging diversity, but limitations persist in other generators. Future research should explore strategies for improving AI to promote a more inclusive and accurate representation of the evolving demographics of orthopaedic surgery, mitigating biases related to race and gender. Clinical Relevance: This study is clinically relevant as it investigates the accuracy of AI-generated images in depicting diversity among orthopaedic surgeons. The findings reveal significant discrepancies in representation by race and gender, which could impact societal perceptions and exacerbate existing disparities in health care.