Photographic and Video Deepfakes Have Arrived: How Machine Learning May Influence Plastic Surgery

被引:25
作者
Crystal, Dustin T.
Cuccolo, Nicholas G.
Ibrahim, Ahmed M. S.
Furnas, Heather
Lin, Samuel J.
机构
[1] Harvard Med Sch, Beth Israel Deaconess Med Ctr, Div Plast Surg, Boston, MA 02115 USA
[2] Stanford Univ, Med Ctr, Div Plast Surg, Stanford, CA 94305 USA
关键词
3-DIMENSIONAL SIMULATED IMAGES; BREAST AUGMENTATION SURGERY; PATIENTS SATISFACTION; RHINOPLASTY; PREDICTION; STANDARDS; ASYMMETRY;
D O I
10.1097/PRS.0000000000006697
中图分类号
R61 [外科手术学];
学科分类号
摘要
Advances in computer science and photography not only are pervasive but are also quantifiably influencing the practice of medicine. Recent progress in both software and hardware technology has translated into the design of advanced artificial neural networks: computer frameworks that can be thought of as algorithms modeled on the human brain. In practice, these networks have computational functions, including the autonomous generation of novel images and videos, frequently referred to as "deepfakes." The technological advances that have resulted in deepfakes are readily applicable to facets of plastic surgery, posing both benefits and harms to patients, providers, and future research. As a specialty, plastic surgery should recognize these concepts, appropriately discuss them, and take steps to prevent nefarious uses. The aim of this article is to highlight these emerging technologies and discuss their potential relevance to plastic surgery.
引用
收藏
页码:1079 / 1086
页数:8
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