The Surgeon's Digital Eye: Assessing Artificial Intelligence-generated Images in Breast Augmentation and Reduction

Given the public's tendency to overestimate the capability of artificial intelligence (AI) in surgical outcomes for plastic surgery, this study assesses the accuracy of AI-generated images for breast augmentation and reduction, aiming to determine if AI technology can deliver realistic expectations and can be useful in a surgical context.

We used AI platforms GetIMG, Leonardo, and Perchance to create pre- and postsurgery images of breast augmentation and reduction. Board-certified plastic surgeons and plastic surgery residents evaluated these images using 11 metrics and divided them into 2 categories: realism and clinical value. Statistical analysis was conducted using analysis of variance and Tukey honestly significant difference post hoc tests. Images of the nipple-areolar complex were excluded due to AI's nudity restrictions.

GetIMG (mean ± SD) (realism: 3.83 ± 0.81, clinical value: 3.13 ± 0.62), Leonardo (realism: 3.30 ± 0.69, clinical value: 2.94 ± 0.47), and Perchance (realism: 2.68 ± 0.77, clinical value: 2.88 ± 0.44) showed comparable realism and clinical value scores with no significant difference (P > 0.05). In specific metrics, GetIMG outperformed significantly in surgical relevance compared with the other models (P values: 0.02 and 0.03). Healing and scarring prediction is the metric that underperformed across models (2.25 ± 1.11 P ≤ 0.03). Panelists found some images "cartoonish" with unrealistic skin, indicating AI origin.

The AI models showed similar performance, with some images accurately predicting postsurgical outcomes, particularly breast size and volume in a bra. Despite this promise, the absence of detailed nipple-areola complex visualization is a significant limitation. Until these features and consistent representations of various body types and skin tones are achievable, the authors advise using actual patient photographs for consultations.