Machine Learning, Deep Learning, Artificial Intelligence and Aesthetic Plastic Surgery: A Qualitative Systematic Review.

Abstract

This systematic review aims to assess the use of machine learning, deep learning, and artificial intelligence in aesthetic plastic surgery. This qualitative systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline. To analyze quality risk-of-bias assessment of all included articles, we used the ROBINS-I tool for non-randomized studies. We searched for studies with the following MeSH terms: Machine Learning OR Deep Learning OR Artificial intelligence AND Plastic surgery on MEDLINE/PubMed, EMBASE, and Cochrane Library, from inception until July 2024 without any filter applied. A total of 2,148 studies were screened and 41 were fully reviewed. We conducted article extraction, screening, and full text review using the rayyan tool. Eighteen studies were ultimately included in this review, describing the use of machine learning, deep learning and artificial intelligence in aesthetic plastic surgery. All studies were published from 2019 to 2024. Articles varied regarding the population studied, type of machine learning (ML), Deep Learning Model (DLM), Artificial Intelligence (AI) used, and aesthetic plastic surgery type. Of the eighteen studies, we included the following aesthetic plastic surgeries: augmentation mastopexy, breast augmentation, reduction mammoplasty, rhinoplasty, facial rejuvenation surgery, including facelift surgery; blepharoplasty, and body contouring. Image-based with AI, ML, and DLMs algorithms were used in these studies to improve human decision-making and identified factors associated with postoperative complications. AI, ML, and DL algorithms offer immense potential to transform the aesthetic plastic surgery field. By meticulously analyzing patient data, these technologies may, in the future, help optimize treatment plans, predict potential complications, and more clearly elucidate patient concerns, improving their ability to make informed decisions. The drawback, as with preoperative surgical simulation, is that patients may see an AI-generated image that is to their liking, but impossible to achieve; great care is needed when using such tools in order to not create unrealistic expectations. Ultimately, the old plastic surgery adage of ''under-promise and over-deliver'' will continue to hold true, at least for the foreseeable future. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . Study registration A review protocol for this systematic review was registered at PROSPERO CRD42024567461.