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Development and Validation of a Machine Learning Tool for Plastic Surgery Residency Application Screening.

Commentary on "Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualit…

TL;DRThe current FDA regulatory pathways relevant to AI applications in plastic surgery, including the 510(k), De Novo, and Premarket Approval processes are outlined and the importance of Good Machine Learning Practices (GMLP…

…-world performance monitoring requirements. We highlight the importance of Good Machine Learning Practices and the collaborative framework developed by FDA, National Institute …

TL;DRImage-based with AI, ML, and DLMs algorithms were used in these studies to improve human decision-making and identified factors associated with postoperative complications, which may help optimize treatment plans, predic…

Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualit…

Identification of Key Breast Features Using a Neural Network: Applications of Machine Learning in the Clinical Setting of Plastic Surgery.

TL;DRAll studies showed that using ML in the facial cosmetic surgeries is accurate enough to benefit both surgeons and patients, and needs further studies, especially in the fields of diagnosis and treatment planning.

Applications of Machine Learning in Facial Cosmetic Surgeries: A Scoping Review.

Applying Machine Learning to Determine Popular Patient Questions About Mentoplasty on Social Media.

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

Big Data and Machine Learning in Plastic Surgery: A New Frontier in Surgical Innovation.

TL;DRChatGPT-4 demonstrated high clinician-rated accuracy and clarity when answering standardized postoperative rhinoplasty questions, while patient-centered communication remained comparatively lower, suggesting that LLM-bas…

Artificial Intelligence in Rhinoplasty Recovery: Linguistic Intelligence and Machine Learning-Driven Insights.

TL;DRA machine learning algorithm was used to identify a sex-specific age threshold beyond which a preoperative chest CT is likely to reveal CCC relevant for rhinoplasty planning, and a chest CT is recommended in females over…

Costal Cartilage Calcification in a Caucasian Population: Machine Learning Recommendations for Chest CT-guided Rhinoplasty Planning.

TL;DRAn HTA was developed for open structural rhinoplasty, then a survey was performed to gather surgeons' self-reported time to complete tasks, and surgical steps that showed statistically significant differences between the…

…uctural Rhinoplasty: A Task-Based Analysis Using Hierarchical Task Analysis and Machine Learning.

Making the Subjective Objective: Machine Learning and Rhinoplasty.

TL;DRA statistically significant correlation between smoking and the desire for implant change was revealed and the importance of implementing artificial intelligence into clinical research could be underpinned by this study,…

Implementation of a Machine Learning Approach Evaluating Risk Factors for Complications after Single-Stage Augmentat…

TL;DRThe findings show that machine learning can accurately predict the needed size of breast implants in augmentation surgery, by integrating the artificial intelligence model into a decision support system for breast augmen…

Using Machine Learning to Select Breast Implant Volume.

TL;DRThe authors' ML resection weight prediction model represents an accurate and promising alternative to the Schnur Scale in the setting of reduction mammaplasty consultations.

Predicting Reduction Mammaplasty Total Resection Weight With Machine Learning.

A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthe…

TL;DRThe neural network outperformed human experts and other machine learning approaches in prediction of objective and patient-reported OBTX-A outcomes for overactive bladder in a challenging independent validation cohort.

External Validation Demonstrates Machine Learning Models Outperform Human Experts in Prediction of Objective and Patient-reported…

TL;DRA quarter of patients with PSS received BoNT-A mainly for pain relief, typically one year after the stroke, among the study cohort, and rehabilitation was the most common non-pharmacological treatment.

…st-Stroke Spasticity: A Multicenter Study Using Natural Language Processing and Machine Learning.

TL;DRA machine learning model is introduced using SVM analysis on ultrasound (USG) images for bruxism patients, which can detect masseter muscle changes on USG and showed the combined ML models can also predict the outcome of…

Development and Validation of an Ultrasonography-Based Machine Learning Model for Predicting Outcomes of Bruxism Treatments.