Background: ChatGPT is an open-source artificial large language model that uses deep learning to produce human-like text dialogue. This observational study evaluated the ability of ChatGPT to provide informative and accurate responses to a set of hypothetical questions designed to simulate an initial consultation about rhinoplasty. Methods: Nine questions were prompted to ChatGPT on rhinoplasty. The questions were sourced from a checklist published by the American Society of Plastic Surgeons, and the responses were assessed for accessibility, informativeness, and accuracy by Specialist Plastic Surgeons with extensive experience in rhinoplasty. Results: ChatGPT was able to provide coherent and easily comprehensible answers to the questions posed, demonstrating its understanding of natural language in a health-specific context. The responses emphasized the importance of an individualized approach, particularly in aesthetic plastic surgery. However, the study also highlighted ChatGPT’s limitations in providing more detailed or personalized advice. Conclusion: Overall, the results suggest that ChatGPT has the potential to provide valuable information to patients in a medical context, particularly in situations where patients may be hesitant to seek advice from medical professionals or where access to medical advice is limited. However, further research is needed to determine the scope and limitations of AI language models in this domain and to assess the potential benefits and risks associated with their use. Level of Evidence V: Observational study under respected authorities. 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.

Background: The integration of artificial intelligence (AI) and machine learning (ML) technologies into healthcare is transforming patient-practitioner interaction and could offer an additional platform for patient education and support. Objectives: This study investigated whether ChatGPT-4 could provide safe and up-to-date medical information about breast augmentation that is comparable to other patient information sources. Methods: ChatGPT-4 was asked to generate 6 commonly asked questions regarding breast augmentation and respond to them. Its responses were qualitatively evaluated by a panel of specialist plastic and reconstructive surgeons and reconciled with a literature search of 2 large medical databases for accuracy, informativeness, and accessibility. Results: ChatGPT-4 provided well-structured, grammatically accurate, and comprehensive responses to the questions posed; however, it was limited in providing personalized advice and sometimes generated inappropriate or outdated references. ChatGPT consistently encouraged engagement with a specialist for specific information. Conclusions: Although ChatGPT-4 showed promise as an adjunct tool in patient education regarding breast augmentation, there are areas requiring improvement. Additional advancements and software engineering are needed to enhance the reliability and applicability of AI-driven chatbots in patient education and support systems.

Background: Artificial intelligence (AI) chatbots, such as ChatGPT, have made significant progress. These chatbots, particularly popular among health care professionals and patients, are transforming patient education and disease experience with personalized information. Accurate, timely patient education is crucial for informed decision-making, especially regarding prostate-specific antigen screening and treatment options. However, the accuracy and reliability of AI chatbots’ medical information must be rigorously evaluated. Studies testing ChatGPT’s knowledge of prostate cancer are emerging, but there is a need for ongoing evaluation to ensure the quality and safety of information provided to patients. Objective: This study aims to evaluate the quality, accuracy, and readability of ChatGPT-4’s responses to common prostate cancer questions posed by patients. Methods: Overall, 8 questions were formulated with an inductive approach based on information topics in peer-reviewed literature and Google Trends data. Adapted versions of the Patient Education Materials Assessment Tool for AI (PEMAT-AI), Global Quality Score, and DISCERN-AI tools were used by 4 independent reviewers to assess the quality of the AI responses. The 8 AI outputs were judged by 7 expert urologists, using an assessment framework developed to assess accuracy, safety, appropriateness, actionability, and effectiveness. The AI responses’ readability was assessed using established algorithms (Flesch Reading Ease score, Gunning Fog Index, Flesch-Kincaid Grade Level, The Coleman-Liau Index, and Simple Measure of Gobbledygook [SMOG] Index). A brief tool (Reference Assessment AI [REF-AI]) was developed to analyze the references provided by AI outputs, assessing for reference hallucination, relevance, and quality of references. Results: The PEMAT-AI understandability score was very good (mean 79.44%, SD 10.44%), the DISCERN-AI rating was scored as “good” quality (mean 13.88, SD 0.93), and the Global Quality Score was high (mean 4.46/5, SD 0.50). Natural Language Assessment Tool for AI had pooled mean accuracy of 3.96 (SD 0.91), safety of 4.32 (SD 0.86), appropriateness of 4.45 (SD 0.81), actionability of 4.05 (SD 1.15), and effectiveness of 4.09 (SD 0.98). The readability algorithm consensus was “difficult to read” (Flesch Reading Ease score mean 45.97, SD 8.69; Gunning Fog Index mean 14.55, SD 4.79), averaging an 11th-grade reading level, equivalent to 15- to 17-year-olds (Flesch-Kincaid Grade Level mean 12.12, SD 4.34; The Coleman-Liau Index mean 12.75, SD 1.98; SMOG Index mean 11.06, SD 3.20). REF-AI identified 2 reference hallucinations, while the majority (28/30, 93%) of references appropriately supplemented the text. Most references (26/30, 86%) were from reputable government organizations, while a handful were direct citations from scientific literature. Conclusions: Our analysis found that ChatGPT-4 provides generally good responses to common prostate cancer queries, making it a potentially valuable tool for patient education in prostate cancer care. Objective quality assessment tools indicated that the natural language processing outputs were generally reliable and appropriate, but there is room for improvement.

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. 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. 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. 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.

Large language models (LLMs) are emerging artificial intelligence (AI) technologies refining research and healthcare. However, the impact of these models on presurgical planning and education remains under-explored. This study aims to assess 3 prominent LLMs—Google's AI BARD (Mountain View, CA), Bing AI (Microsoft, Redmond, WA), and ChatGPT-3.5 (Open AI, San Francisco, CA) in providing safe medical information for rhinoplasty. Six questions regarding rhinoplasty were prompted to ChatGPT, BARD, and Bing AI. A Likert scale was used to evaluate these responses by a panel of Specialist Plastic and Reconstructive Surgeons with extensive experience in rhinoplasty. To measure reliability, the Flesch Reading Ease Score, the Flesch–Kincaid Grade Level, and the Coleman–Liau Index were used. The modified DISCERN score was chosen as the criterion for assessing suitability and reliability. A t test was performed to calculate the difference between the LLMs, and a double-sided P-value <.05 was considered statistically significant. In terms of reliability, BARD and ChatGPT demonstrated a significantly (P < .05) greater Flesch Reading Ease Score of 47.47 (±15.32) and 37.68 (±12.96), Flesch–Kincaid Grade Level of 9.7 (±3.12) and 10.15 (±1.84), and a Coleman–Liau Index of 10.83 (±2.14) and 12.17 (±1.17) than Bing AI. In terms of suitability, BARD (46.3 ± 2.8) demonstrated a significantly greater DISCERN score than ChatGPT and Bing AI. In terms of Likert score, ChatGPT and BARD demonstrated similar scores and were greater than Bing AI. BARD delivered the most succinct and comprehensible information, followed by ChatGPT and Bing AI. Although these models demonstrate potential, challenges regarding their depth and specificity remain. Therefore, future research should aim to augment LLM performance through the integration of specialized databases and expert knowledge, while also refining their algorithms.

Aram Cox, MD, Ishith Seth, Bbiomed (Hons), MD, Yi Xie, LLB (Hons), BA, Dip Lang, MD, David J Hunter-Smith, MBBS, MPH, FRACS, FACS, Warren M Rozen, MBBS, MD, BMe