AI in Cosmetic Surgery: A New Look at Virtual Abdominoplasty and Buttock Augmentation.

Background: Online before-and-after photos commonly guide patient expectations in body contouring surgeries. However, recent artificial intelligence (AI) advancements allow for lifelike "photos" of hypothetical individuals, which patients can use in their decision-making. The accuracy of AI models, trained on divergent image sets, in showing realistic figures, cosmetic defects, and surgical outcomes is questionable. This study sought to evaluate the quality of these images.

Methods: We utilized AI platforms GetIMG, Leonardo, and Perchance to create pre- and post-surgery visuals for abdominoplasty and buttock augmentation. Expert board-certified plastic surgeons and plastic surgery residents assessed the images across 11 criteria, focusing on realism and clinical value. ANOVA and Tukey honestly significant difference post-hoc tests were executed for data analysis.

Results: Realism and clinical value scores among AI models (mean ± standard deviation) were not significantly different, indicating comparable performance (GetIMG 3.83 ± 0.81, Leonardo 3.30 ± 0.69, Perchance 2.68 ± 0.77; > .05). Perchance significantly underperformed in size and volume accuracy ( = .02) and pathological feature recognition ( = .01 and .03). No consistent underperforming metric was identified when evaluated. The phenomenon of the "uncanny valley" was also identified.

Conclusions: Despite some realistic and accurate surgical predictions, most AI-generated images were anatomically unrealistic, demonstrated inaccurate postoperative results, and invoked the "uncanny valley" effect. Given the uniformly poor performance, patients should avoid using these images for surgical decisions due to the potential of unrealistic expectations. Surgeons are advised to use real patient photos for consultations. Future research aims to compare AI images with actual before-and-after photos and include a bigger pool of experts for evaluation.