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

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

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.

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.