A Systematic Review of Artificial Intelligence Applications in Plastic Surgery: Looking to the Future

The Role of AI in Modern Hernia Surgery: A Review and Practical Insights

BackgroundArtificial intelligence (AI) is revolutionizing various aspects of health care, particularly in the surgical field, where it offers significant potential for improving surgical risk assessment, predictive analytics, and research advancement. Despite the development of numerous AI models in surgery, there remains a notable gap in understanding their specific application within the context of hernia surgery.PurposeThis review aims to explore the evolution of AI utilization in hernia surgery over the past 2 decades, focusing on the contributions of Machine Learning (ML), Natural Language Processing (NLP), Computer Vision (CV), and Robotics.ResultsWe discuss how these AI fields enhance surgical outcomes and advance research in the domain of hernia surgery. ML focuses on developing and training prediction models, while NLP enables seamless human-computer interaction through the use of Large Language Models (LLMs). CV assists in critical view detection, which is crucial in procedures such as inguinal hernia repair, and robotics improves minimally invasive techniques, dexterity, and precision. We examine recent evidence and the applicability of various AI models on hernia patients, considering the strengths, limitations, and future possibilities within each field.ConclusionBy consolidating the impact of AI models on hernia surgery, this review provides insights into the potential of AI for advancing patient care and surgical techniques in this field, ultimately contributing to the ongoing evolution of surgical practice.

The Transformative Role of Artificial Intelligence in Plastic and Reconstructive Surgery: Challenges and Opportunities

Background/Objectives: This study comprehensively examines how artificial intelligence (AI) technologies are transforming clinical practice in plastic and reconstructive surgery across the entire patient care continuum, with the specific objective of identifying evidence-based applications, implementation challenges, and emerging opportunities that will shape the future of the specialty. Methods: A comprehensive narrative review was conducted analyzing the integration of AI technologies in plastic surgery, including preoperative planning, intraoperative applications, postoperative monitoring, and quality improvement. Challenges related to implementation, ethics, and regulatory frameworks were also examined, along with emerging technological trends that will shape future practice. Results: AI applications in plastic surgery demonstrate significant potential across multiple domains. In preoperative planning, AI enhances risk assessment, outcome prediction, and surgical simulation. Intraoperatively, AI-assisted robotics enables increased precision and technical capabilities beyond human limitations, particularly in microsurgery. Postoperatively, AI improves complication detection, pain management, and outcomes assessment. Despite these benefits, implementation faces challenges including data privacy concerns, algorithmic bias, liability questions, and the need for appropriate regulatory frameworks. Future directions include multimodal AI systems, federated learning approaches, and integration with extended reality and regenerative medicine technologies. Conclusions: The integration of AI into plastic surgery represents a significant opportunity to enhance surgical precision, improve outcome prediction, and expand the boundaries of what is surgically possible. However, successful implementation requires addressing ethical considerations and maintaining the human elements of surgical care. Plastic surgeons must actively engage with AI development to ensure these technologies address genuine clinical needs while aligning with the specialty's core values of restoring form and function, alleviating suffering, and enhancing quality of life.

Assessing the Informational Value of Large Language Models Responses in Aesthetic Surgery: A Comparative Analysis with Expert Opinions

BACKGROUND

The increasing popularity of Large Language Models (LLMs) in various healthcare settings has raised questions about their ability to provide accurate and reliable information. This study aimed to evaluate the informational value of Large Language Models responses in aesthetic plastic surgery by comparing them with the opinions of experienced surgeons.

METHODS

Thirty patients undergoing three common aesthetic procedures-dermal fillers, botulinum toxin injections, and aesthetic blepharoplasty-were selected. The most frequently asked questions by these patients were recorded and submitted to ChatGpt 3.5 and Google Bard v.1.53. The answers provided by the Large Language Models were then evaluated by 13 experienced aesthetic plastic surgeons on a Likert scale for accessibility, accuracy, and overall usefulness.

RESULTS

The overall ratings of the chatbot responses were moderate, with surgeons generally finding them to be accurate and clear. However, the lack of transparency regarding the sources of the information provided by the LLMs made it impossible to fully evaluate their credibility.

CONCLUSIONS

While chatbots have the potential to provide patients with convenient access to information about aesthetic plastic surgery, their current limitations in terms of transparency and comprehensiveness warrant caution in their use as a primary source of information. Further research is needed to develop more robust and reliable LLMs for healthcare applications.

LEVEL OF EVIDENCE I

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 .

Advancements in microsurgery: A comprehensive systematic review of artificial intelligence applications

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and discern patterns without direct instruction. This review explores cutting-edge developments in microsurgery through the lens of AI applications. By analyzing a wide range of studies, this paper highlights AI's transformative role in enhancing microsurgical techniques and decision-making processes. A systematic literature search was conducted using Ovid MEDLINE, Ovid Embase, Web of Science, and PubMed (2005-2023). Extensive data on ML model function and composition, as well as broader study characteristics, were collected from each study. Study quality was assessed across 7 methodological areas of AI research using an adapted methodological index of nonrandomized studies (MINORS) tool. Seventeen studies met the inclusion criteria. ML was used primarily for prognosis (35%), postoperative assessment (29%), and intraoperative assistance/robotic surgery (24%). Only 2 studies were conducted beyond phase 0 of AI research. Fourteen studies included a training group, but only one of these reported both validation and training sets. ML model performance was assessed most frequently using accuracy, specificity, and sensitivity. Scores for the adapted MINORS criteria ranged from 10 to 14 out of 14, with a median of 12. Through collation of all available preclinical and clinical trials, this review suggests the efficacy of ML for various microsurgical applications. Despite this, widespread adoption of this technology remains scarce, currently limited by methodological flaws of individual studies and structural barriers to disruptive technologies. However, with growing evidence supporting its use, microsurgeons should be receptive to implementing ML-incorporated technologies or may risk falling behind other specialties.

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

PURPOSE

This systematic review aims to assess the use of machine learning, deep learning, and artificial intelligence in aesthetic plastic surgery.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

LEVEL OF EVIDENCE III

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.

From Data to Decisions: How Artificial Intelligence Is Revolutionizing Clinical Prediction Models in Plastic Surgery

SUMMARY

The impact of clinical prediction models within artificial intelligence (AI) and machine learning is significant. With its ability to analyze vast amounts of data and identify complex patterns, machine learning has the potential to improve and implement evidence-based plastic, reconstructive, and hand surgery. In addition, it is capable of predicting the diagnosis, prognosis, and outcomes of individual patients. This modeling aids daily clinical decision-making, most commonly at the moment, as decision support. The purpose of this article is to provide a practice guideline to plastic surgeons implementing AI in clinical decision-making or setting up AI research to develop clinical prediction models using the 7-step approach and the ABCD validation steps of Steyerberg and Vergouwe. The authors also describe 2 important protocols that are in the development stage for AI research: (1) the transparent reporting of a multivariable prediction model for Individual Prognosis or Diagnosis checklist, and (2) the Prediction Model Risk of Bias Assessment Tool checklist to access potential biases.

Decoding the Impact of AI on Microsurgery: Systematic Review and Classification of Six Subdomains for Future Development

Background

The advent of artificial intelligence (AI) in microsurgery has tremendous potential in plastic and reconstructive surgery, with possibilities to elevate surgical precision, planning, and patient outcomes. This systematic review seeks to summarize available studies on the implementation of AI in microsurgery and classify these into subdomains where AI can revolutionize our field.

Methods

Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, a meticulous search strategy was used across multiple databases. The inclusion criteria encompassed articles that explicitly discussed AI's integration in microsurgical practices. Our aim was to analyze and classify these studies across subdomains for future development.

Results

The search yielded 2377 articles, with 571 abstracts eligible for screening. After shortlisting and reviewing 86 full-text articles, 29 studies met inclusion criteria. Detailed analysis led to the classification of 6 subdomains within AI applications in microsurgery, including information and knowledge delivery, microsurgical skills training, preoperative planning, intraoperative navigational aids and automated surgical tool control, flap monitoring, and postoperative predictive analytics for patient outcomes. Each subtheme showcased the multifaceted impact of AI on enhancing microsurgical procedures, from preoperative planning to postoperative recovery.

Conclusions

The integration of AI into microsurgery signals a new dawn of surgical innovation, albeit with the caution warranted by its nascent stage and application diversity. The authors present a systematic review and 6 clear subdomains across which AI will likely play a role within microsurgery. Continuous research, ethical diligence, and cross-disciplinary cooperation is necessary for its successful integration within our specialty.

Artificial Intelligence in Facial Plastic and Reconstructive Surgery: A Systematic Review

Artificial intelligence (AI) is a technology that is evolving rapidly and is changing the world and medicine as we know it. After reviewing the PROSPERO database of systematic reviews, there is no article related to this topic in facial plastic and reconstructive surgery. The objective of this article was to review the literature regarding AI applications in facial plastic and reconstructive surgery.A systematic review of the literature about AI in facial plastic and reconstructive surgery using the following keywords: Artificial Intelligence, robotics, plastic surgery procedures, and surgery plastic and the following databases: PubMed, SCOPUS, Embase, BVS, and LILACS. The inclusion criteria were articles about AI in facial plastic and reconstructive surgery. Articles written in a language other than English and Spanish were excluded. In total, 17 articles about AI in facial plastic met the inclusion criteria; after eliminating the duplicated papers and applying the exclusion criteria, these articles were reviewed thoroughly. The leading type of AI used in these articles was computer vision, explicitly using models of convolutional neural networks to objectively compare the preoperative with the postoperative state in multiple interventions such as facial lifting and facial transgender surgery.In conclusion, AI is a rapidly evolving technology, and it could significantly impact the treatment of patients in facial plastic and reconstructive surgery. Legislation and regulations are developing slower than this technology. It is imperative to learn about this topic as soon as possible and that all stakeholders proactively promote discussions about ethical and regulatory dilemmas.

Artificial Intelligence in Facial Plastics and Reconstructive Surgery

Artificial intelligence (AI), particularly computer vision and large language models, will impact facial plastic and reconstructive surgery (FPRS) by enhancing diagnostic accuracy, refining surgical planning, and improving post-operative evaluations. These advancements can address subjective limitations of aesthetic surgery by providing objective tools for patient evaluation. Despite these advancements, AI in FPRS has yet to be fully integrated in the clinic setting and faces numerous challenges including algorithmic bias, ethical considerations, and need for validation. This article discusses current and emerging AI technologies in FPRS for the clinic setting, providing a glimpse of its future potential.

Exploring the Unknown: Evaluating ChatGPT's Performance in Uncovering Novel Aspects of Plastic Surgery and Identifying Areas for Future Innovation

BACKGROUND

Artificial intelligence (AI) has emerged as a powerful tool in various medical fields, including plastic surgery. This study aims to evaluate the performance of ChatGPT, an AI language model, in elucidating historical aspects of plastic surgery and identifying potential avenues for innovation.

METHODS

A comprehensive analysis of ChatGPT's responses to a diverse range of plastic surgery-related inquiries was performed. The quality of the AI-generated responses was assessed based on their relevance, accuracy, and novelty. Additionally, the study examined the AI's ability to recognize gaps in existing knowledge and propose innovative solutions. ChatGPT's responses were analysed by specialist plastic surgeons with extensive research experience, and quantitatively analysed with a Likert scale.

RESULTS

ChatGPT demonstrated a high degree of proficiency in addressing a wide array of plastic surgery-related topics. The AI-generated responses were found to be relevant and accurate in most cases. However, it demonstrated convergent thinking and failed to generate genuinely novel ideas to revolutionize plastic surgery. Instead, it suggested currently popular trends that demonstrate great potential for further advancements. Some of the references presented were also erroneous as they cannot be validated against the existing literature.

CONCLUSION

Although ChatGPT requires major improvements, this study highlights its potential as an effective tool for uncovering novel aspects of plastic surgery and identifying areas for future innovation. By leveraging the capabilities of AI language models, plastic surgeons may drive advancements in the field. Further studies are needed to cautiously explore the integration of AI-driven insights into clinical practice and to evaluate their impact on patient outcomes.

LEVEL OF EVIDENCE V

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.

Aesthetically Ideal Breasts Created With Artificial Intelligence: Validating the Literature, Racial Differences, and Deep Fakes

Background

A female's breasts are integrally tied to her identity and sense of femininity. Despite extensive study of breast aesthetics, there is no discrete formula for the "ideal breast" to guide the aesthetic surgeon. Racial and cultural differences heavily influence preferences in breast morphology. Artificial intelligence (AI) is ubiquitous in modern culture and may aid in further understanding ideal breast aesthetics.

Objectives

This study analyzed AI-generated images of aesthetically ideal breasts, evaluated for morphologic differences based on race, and compared findings to the literature.

Methods

An openly accessible AI image-generator platform was used to generate images of aesthetically ideal Caucasian, African American, and Asian breasts in 3-quarter profile and frontal views using simple text prompts. Breast measurements were obtained and compared between each racial cohort and to that of previously described ideal breast parameters.

Results

Twenty-five images were analyzed per racial cohort, per pose (150 total). Caucasian breasts were observed to fit nicely into previously described ideal breast templates. However, upper-to-lower pole ratios, nipple angles, upper pole slope contours, nipple-areolar complex positions, and areolar size were observed to have statistically significant differences between racial cohorts.

Conclusions

Defining the aesthetically ideal breast remains a complex and multifaceted challenge, requiring consideration of racial and cultural differences. The AI-generated breasts in this study were found to have significant differences between racial groups, support several previously described breast ideals, and provide insight into current and future ethical issues related to AI in aesthetic surgery.

Level of Evidence 5

Current Status of Auricular Reconstruction Strategy Development

Microtia has severe physical and psychological impacts on patients, and auricular reconstruction offers improved esthetics and function, alleviating psychological issues. Microtia is a congenital disease caused by a multifactorial interaction of environmental and genetic factors, with complex clinical manifestations. Classification assessment aids in determining treatment strategies. Auricular reconstruction is the primary treatment for severe microtia, focusing on the selection of auricular scaffold materials, the construction of auricular morphology, and skin and soft tissue scaffold coverage. Autologous rib cartilage and synthetic materials are both used as scaffold materials for auricular reconstruction, each with advantages and disadvantages. Methods for achieving skin and soft tissue scaffold coverage have been developed to include nonexpansion and expansion techniques. In recent years, the application of digital auxiliary technology such as finite element analysis has helped optimize surgical outcomes and reduce complications. Tissue-engineered cartilage scaffolds and 3-dimensional bioprinting technology have rapidly advanced in the field of ear reconstruction. This article discusses the prevalence and classification of microtia, the selection of auricular scaffolds, the evolution of surgical methods, and the current applications of digital auxiliary technology in ear reconstruction, with the aim of providing clinical physicians with a reference for individualized ear reconstruction surgery. The focus of this work is on the current applications and challenges of tissue engineering and 3-dimensional bioprinting technology in the field of ear reconstruction, as well as future prospects.

Building Complex Autologous Breast Reconstruction Program: A Preliminary Experience

Autologous breast reconstruction is an increasingly popular method of reconstruction for breast cancer survivors. While deep inferior epigastric perforator (DIEP) flaps are the gold standard, not all patients are ideal candidates for DIEP flaps due to low BMI, body habitus, or previous abdominal surgery. In these patients, complex autologous breast reconstruction can be performed, but there is a limited number of programs around the world due to high technical demand. Given the increased demand and need for complex autologous flaps, it is critical to build programs to increase patient access and teach future microsurgeons. In this paper, we discuss the steps, pearls, and preliminary experience of building a complex autologous breast reconstruction program in a tertiary academic center. We performed a retrospective chart review of patients who underwent starting the year prior to the creation of our program. Since the start of our program, a total of 74 breast mounds have been reconstructed in 46 patients using 87 flaps. Over 23 months, there was a decrease in median surgical time for bilateral reconstruction by 124 min (p = 0.03), an increase in the number of co-surgeon cases by 66% (p < 0.01), and an increase in the number of complex autologous breast reconstruction by 42% (p < 0.01). Our study shows that a complex autologous breast reconstruction program can be successfully established using a multi-phase approach, including the development of a robust co-surgeon model. In addition, we found that a dedicated program leads to increased patient access, decreased operative time, and enhancement of trainee education.

Using Generative Artificial Intelligence Tools in Cosmetic Surgery: A Study on Rhinoplasty, Facelifts, and Blepharoplasty Procedures

Artificial intelligence (AI), notably Generative Adversarial Networks, has the potential to transform medical and patient education. Leveraging GANs in medical fields, especially cosmetic surgery, provides a plethora of benefits, including upholding patient confidentiality, ensuring broad exposure to diverse patient scenarios, and democratizing medical education. This study investigated the capacity of AI models, DALL-E 2, Midjourney, and Blue Willow, to generate realistic images pertinent to cosmetic surgery. We combined the generative powers of ChatGPT-4 and Google's BARD with these GANs to produce images of various noses, faces, and eyelids. Four board-certified plastic surgeons evaluated the generated images, eliminating the need for real patient photographs. Notably, generated images predominantly showcased female faces with lighter skin tones, lacking representation of males, older women, and those with a body mass index above 20. The integration of AI in cosmetic surgery offers enhanced patient education and training but demands careful and ethical incorporation to ensure comprehensive representation and uphold medical standards.