How Machine Learning May Influence Plastic Surgery

Abstract and Introduction

Abstract

Summary: 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.

Introduction

In the modern era of social media, the number of likes, dislikes, and comments seemingly translates into a social sign of worth. These otherwise valueless tokens are a means of propagating ideas, education, news, and potentially revenue. Throughout modern history, the transition from blogs to applications such as Twitter and Instagram was rapid. The current question is not only "What is the next Twitter or Instagram?" but also "What is the next venue of social media technology, and how might it apply to plastic and reconstructive surgery?"

As a field that quantifies success with reconstructive and aesthetic outcomes, the practice of plastic surgery necessitates documentation of outcomes through standardized photographic images.[1,2] These pictures function as a tool to not only track and evaluate outcomes but also as means for education/advertisement. With the advent of photograph editing software, the digital retouching of images has become more far-reaching in its ability to transform and manipulate results. As such, proposed new standards for consistent plastic surgery photographs recognize the role of photograph editing in terms of both the ubiquitous ability to digitally retouch images and its ability to objectively standardize photographs in terms of size, contrast, and brightness.[3]

More recently, there have been significant transitions in technology and machine learning. Particularly, technological advances in software and hardware have translated into the design of artificial neural networks; these are computer frameworks based on algorithms modeled on the human brain. In practice, these networks have computational functions, such as having the ability to generate novel images and videos.[4] The purpose of this article is to highlight these emerging technologies and discuss their relevance to plastic surgery.

 Citește tot articolul aici:https://www.medscape.com/viewarticle/928124_1

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