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For years, plastic surgeons thought the proportions of a beautiful buttocks should follow the Fibonacci sequence. Now, people are looking for a more Kardashian shape. In the history of gluteal enhancement, Mexico City stands out. It was here, in 1979, that a plastic surgeon, Mario González-Ulloa, first installed a pair of silicone implants designed specifically for the buttocks. The textbook calls González-Ulloa the "grandfather of buttock augmentation." The early 2000s saw a new generation of Mexico City buttock transformation luminaries, notably Ramón Cuenca-Guerra. Cuenca-Guerra laid out four characteristics that "determine attractive buttocks" as well as the five types of "defects," with strategies for correcting each one. I, for instance, have defect type 5, the "senile buttock." While I understand the value of standardizing procedures and setting guidelines for surgical practice, I tripped over Cuenca-Guerra's methodology. How and by whom had the determinants been determined?

Vascular anastomosis, the surgical connection of blood vessels, is essential in procedures such as organ transplants and reconstructive surgeries. The precision required limits accessibility due to the extensive training needed, with manual suturing leading to variable outcomes and revision rates up to 7.9%. Existing robotic systems, while promising, are either fully teleoperated or lack the capabilities necessary for autonomous vascular anastomosis. We present the Micro Smart Tissue Autonomous Robot (micro-STAR), an autonomous robotic system designed to perform vascular anastomosis on small-diameter vessels. The micro-STAR system integrates a novel suturing tool equipped with Optical Coherence Tomography (OCT) fiber-optic sensor and a microcamera, enabling real-time tissue detection and classification. Our system autonomously places sutures and manipulates tissue with minimal human intervention. In an ex vivo study, micro-STAR achieved outcomes competitive with experienced surgeons in terms of leak pressure, lumen reduction, and suture placement variation, completing 90% of sutures without human intervention. This represents the first instance of a robotic system autonomously performing vascular anastomosis on real tissue, offering significant potential for improving surgical precision and expanding access to high-quality care.

Dr. Sheila Nazarian, the star of Netflix's "Skin Decision: Before and After," said celebrities who want to speak out on the Israel-Hamas war should educate themselves first. A woman in Arizona has sued her plastic surgeon, accusing him of botching her procedure while operating under the influence of alcohol, leaving her in distress, according to local reports. Dr. Bradley Becker is a "Double Board Certified-Plastic Reconstructive surgeon who has been practicing in AZ for 21 years. "It's hard to feel like you can go out when you feel like Frankenstein," his former patient, Wendy Ellsworth, said in an interview Friday with Phoenix New Times. Ellsworth said she got a tummy tuck and breast reduction with Dr. Becker. Ellsworth sued Becker in Maricopa County Superior Court in September, accusing the Glendale plastic and reconstructive surgeon of "medical negligence," "battery" and "intentional infliction of emotional distress," the report said. Woman said she felt like Frankenstein after plastic surgery allegedly went wrong. Ellsworth said she thought she smelled alcohol when Dr. Becker came to see her before the operation began. "I had put my money down.

Dr. Ben Talei, who was recently publicly thanked by Sia for her facelift, told Fox News Digital he did'a ton' of facelifts during the height of the actors' strike. During the strike, actors have found themselves with a lot of downtime. In addition to picketing, another popular option, according to Dr. Ben Talei, is getting a cosmetic refresh. The Beverly Hills-based plastic surgeon, who was recently praised by "Chandelier" singer Sia for giving her an "amazing" facelift, explained the mini plastic-surgery boom he has seen in his office. "Before the strike, as rumors were kind of going around that a strike was going to start… I began getting consults and I started getting lots of text messages from friends and friends of friends in Hollywood," Talei told Fox News Digital.

The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases based on the sheer volume and velocity of textual data. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. Using a word ranking method, term frequency-inverse document frequency (TF-IDF), to create features across documents, it is possible to perform unsupervised analytics, machine learning (ML) that can group the documents without a human manually labeling the data. For large datasets with thousands of features, t-distributed stochastic neighbor embedding (t-SNE), k-means clustering and Latent Dirichlet allocation (LDA) are employed to learn top words and generate topics for a Reddit and Twitter combined corpus. Using extremely simple deep learning models, this study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery based on a tweet or subreddit post with almost 90% accuracy. Furthermore, the model is capable of achieving higher accuracy on the unsupervised sentiment task than on a rudimentary supervised document classification task. Therefore, unsupervised learning may be considered a viable option in labeling social media documents for NLP tasks.

This piece originally appeared in The Unpublishable, a newsletter critiquing the beauty industry. Even before you grab your bags from the carousels at Seoul's Incheon International Airport, you can fit your face into a spectral imaging machine to get your skin qualitatively analyzed for its health relative to your age. The A.I.-powered analysis is a free service courtesy of the Korea Tourism Organization's Medical Tourism Support Center. Staffers there to greet incoming travelers can usually speak at least some English, Chinese, Japanese, and Russian. Kiosks and clerks can help you find a facility for acupuncture or joint therapy.

Researchers have used artificial intelligence to create'ideal' pageant queen candidates as part of a study to explore the beauty standards of Miss United Kingdom and other global contests. The experts at Great Green Wall used online image generator Midjourney to do this, which gave a surprising variety of results for each country. While Miss United Kingdom was thought to have been influenced by Princess Diana, other nations were inspired by athletes, Bollywood and even Marilyn Monroe. Yet these images often included'highly unobtainable body proportions', researchers said, with'supermodel-like facial structures that can only be achieved through cosmetic surgery or genetics'. Founder of Great Green Wall, Sam Phoenix, wrote: 'Beauty standards can vary drastically from country to country, so it was fascinating to see how well the AI was able to recreate those unique beauty standards within a "pageant" setting.

The Plastic Surgery In-Service Training Exam (PSITE) is an important indicator of resident proficiency and serves as a useful benchmark for evaluating OpenAI's GPT. Unlike many of the simulated tests or practice questions shown in the GPT-4 Technical Paper, the multiple-choice questions evaluated here are authentic PSITE questions. These questions offer realistic clinical vignettes that a plastic surgeon commonly encounters in practice and scores highly correlate with passing the written boards required to become a Board Certified Plastic Surgeon. Our evaluation shows dramatic improvement of GPT-4 (without vision) over GPT-3.5 with both the 2022 and 2021 exams respectively increasing the score from 8th to 88th percentile and 3rd to 99th percentile. The final results of the 2023 PSITE are set to be released on April 11, 2023, and this is an exciting moment to continue our research with a fresh exam. Our evaluation pipeline is ready for the moment that the exam is released so long as we have access via OpenAI to the GPT-4 API. With multimodal input, we may achieve superhuman performance on the 2023.

Automated Program Repair (APR) aspires to automatically generate patches for an input buggy program. Traditional APR tools typically focus on specific bug types and fixes through the use of templates, heuristics, and formal specifications. However, these techniques are limited in terms of the bug types and patch variety they can produce. As such, researchers have designed various learning-based APR tools with recent work focused on directly using Large Language Models (LLMs) for APR. While LLM-based APR tools are able to achieve state-of-the-art performance on many repair datasets, the LLMs used for direct repair are not fully aware of the project-specific information such as unique variable or method names. The plastic surgery hypothesis is a well-known insight for APR, which states that the code ingredients to fix the bug usually already exist within the same project. Traditional APR tools have largely leveraged the plastic surgery hypothesis by designing manual or heuristic-based approaches to exploit such existing code ingredients. However, as recent APR research starts focusing on LLM-based approaches, the plastic surgery hypothesis has been largely ignored. In this paper, we ask the following question: How useful is the plastic surgery hypothesis in the era of LLMs? Interestingly, LLM-based APR presents a unique opportunity to fully automate the plastic surgery hypothesis via fine-tuning and prompting. To this end, we propose FitRepair, which combines the direct usage of LLMs with two domain-specific fine-tuning strategies and one prompting strategy for more powerful APR. Our experiments on the widely studied Defects4j 1.2 and 2.0 datasets show that FitRepair fixes 89 and 44 bugs (substantially outperforming the best-performing baseline by 15 and 8), respectively, demonstrating a promising future of the plastic surgery hypothesis in the era of LLMs.