Can AI Answer My Questions? Utilizing Artificial Intelligence in the Perioperative Assessment for Abdominoplasty Patients.

BACKGROUND: Abdominoplasty is a common operation, used for a range of cosmetic and functional issues, often in the context of divarication of recti, significant weight loss, and after pregnancy. Despite this, patient-surgeon communication gaps can hinder informed decision-making. The integration of large language models (LLMs) in healthcare offers potential for enhancing patient information. This study evaluated the feasibility of using LLMs for answering perioperative queries. METHODS: This study assessed the efficacy of four leading LLMs-OpenAI's ChatGPT-3.5, Anthropic's Claude, Google's Gemini, and Bing's CoPilot-using fifteen unique prompts. All outputs were evaluated using the Flesch-Kincaid, Flesch Reading Ease score, and Coleman-Liau index for readability assessment. The DISCERN score and a Likert scale were utilized to evaluate quality. Scores were assigned by two plastic surgical residents and then reviewed and discussed until a consensus was reached by five plastic surgeon specialists. RESULTS: ChatGPT-3.5 required the highest level for comprehension, followed by Gemini, Claude, then CoPilot. Claude provided the most appropriate and actionable advice. In terms of patient-friendliness, CoPilot outperformed the rest, enhancing engagement and information comprehensiveness. ChatGPT-3.5 and Gemini offered adequate, though unremarkable, advice, employing more professional language. CoPilot uniquely included visual aids and was the only model to use hyperlinks, although they were not very helpful and acceptable, and it faced limitations in responding to certain queries. CONCLUSION: ChatGPT-3.5, Gemini, Claude, and Bing's CoPilot showcased differences in readability and reliability. LLMs offer unique advantages for patient care but require careful selection. Future research should integrate LLM strengths and address weaknesses for optimal patient education. 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 .

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.

Impact of nutrition on skin wound healing and aesthetic outcomes: A comprehensive narrative review.

Background: While current wound treatment strategies often focus on antimicrobials and topical agents, the role of nutrition in wound healing and aesthetic outcomes is crucial but frequently overlooked. This review assesses the impact of specific nutrients and preoperative nutritional status on surgical outcomes. Methods: A comprehensive search was conducted in PubMed, Scopus, Web of Science, and the Cochrane Library, from the inception of the study to October 2023. The study focused on the influence of macronutrients and micronutrients on aesthetic outcomes, the optimization of preoperative nutritional status, and the association between nutritional status and postoperative complications. Inclusion criteria were English language peer-reviewed articles, systematic reviews, meta-analyses, and clinical trials related to the impact of nutrition on skin wound healing and aesthetic outcomes. Exclusion criteria included non-English publications, non-peer-reviewed articles, opinion pieces, and animal studies. Results: Omega-3 fatty acids and specific amino acids were linked to enhanced wound-healing and immune function. Vitamins A, B, and C and zinc positively influenced healing stages, while vitamin E showed variable results. Polyphenolic compounds showed anti-inflammatory effects beneficial for recovery. Malnutrition was associated with increased postoperative complications and infections, whereas preoperative nutritional support correlated with reduced hospital stays and complications. Conclusion: Personalized nutritional plans are essential in surgical care, particularly for enhanced recovery after surgery protocols. Despite the demonstrated benefits of certain nutrients, gaps in research, particularly regarding elements such as iron, necessitate further studies. Nutritional assessments and interventions are vital for optimal preoperative care, underscoring the need for more comprehensive guidelines and research in nutritional management for surgical patients.

Improving Visualization of Intramuscular Perforator Course: Augmented Reality Headsets for DIEP Flap Breast Reconstruction.

Background: Augmented reality (AR) technology, exemplified by devices such as the Microsoft HoloLens 2, has gained interest for its potential applications in preoperative guidance. This study explores the use of AR technology for perforator identification during deep inferior epigastric artery perforator (DIEP) flap breast reconstruction. Methods: A case series of five patients where an AR device was used to identify perforators during DIEP flap breast reconstruction is presented. The device was utilized to recognize preoperative perforators and map their extra- and intramuscular routes. Sound and/or color Doppler confirmation was used to verify the findings. Results: In all five cases, the AR device successfully identified preoperative perforators and delineated their extra- and intramuscular routes. AR technology in perioperative visualization of vasculature offers the potential to enhance surgical precision and reduce operative times. By providing an augmented three-dimensional overlay of patients' vascular structures, AR can facilitate a more comprehensive understanding of individual anatomy, ultimately improving surgical outcomes. Conclusions: AR technology shows promise in enhancing perforator identification efficiency and deepening understanding of perforator trajectories during preoperative planning. Nonetheless, additional research is needed to establish whether the advantages of AR technology warrant its widespread adoption for perforator identification.