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

AI Article Synopsis

Article Abstract

Background: 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.

Methods: 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.

Results: 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 ( > 0.05). In specific metrics, GetIMG outperformed significantly in surgical relevance compared with the other models ( values: 0.02 and 0.03). Healing and scarring prediction is the metric that underperformed across models (2.25 ± 1.11 ≤ 0.03). Panelists found some images "cartoonish" with unrealistic skin, indicating AI origin.

Conclusions: 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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11661769PMC
http://dx.doi.org/10.1097/GOX.0000000000006295DOI Listing

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