TY - JOUR
T1 - The Surgeon's Digital Eye
T2 - Assessing Artificial Intelligence–generated Images in Breast Augmentation and Reduction
AU - Yassa, Arsany
AU - Akhavan, Arya
AU - Ayad, Solina
AU - Ayad, Olivia
AU - Colon, Anthony
AU - Ignatiuk, Ashley
N1 - Publisher Copyright:
© 2024 The Authors.
PY - 2024/12/20
Y1 - 2024/12/20
N2 - 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 (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. 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.
AB - 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 (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. 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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U2 - 10.1097/GOX.0000000000006295
DO - 10.1097/GOX.0000000000006295
M3 - Article
AN - SCOPUS:85213042230
SN - 2169-7574
VL - 12
SP - e6295
JO - Plastic and Reconstructive Surgery - Global Open
JF - Plastic and Reconstructive Surgery - Global Open
IS - 12
ER -