Preoperative Patient Guidance and Education in Aesthetic Breast Plastic Surgery: A Novel Proposed Application of Artificial Intelligence Large Language Models.

Background: At a time when Internet and social media use is omnipresent among patients in their self-directed research about their medical or surgical needs, artificial intelligence (AI) large language models (LLMs) are on track to represent hallmark resources in this context. Objectives: The authors aim to explore and assess the performance of a novel AI LLM in answering questions posed by simulated patients interested in aesthetic breast plastic surgery procedures. Methods: A publicly available AI LLM was queried using simulated interactions from the perspective of patients interested in breast augmentation, mastopexy, and breast reduction. Questions posed were standardized and categorized under aesthetic needs inquiries and awareness of appropriate procedures; patient candidacy and indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; postprocedure instructions and recovery; and procedure cost and surgeon recommendations. Using standardized Likert scales ranging from 1 to 10, 4 expert breast plastic surgeons evaluated responses provided by AI. A postparticipation survey assessed expert evaluators' experience with LLM technology, perceived utility, and limitations. Results: The overall performance across all question categories, assessment criteria, and procedures examined was 7.3/10 ± 0.5. Overall accuracy of information shared was scored at 7.1/10 ± 0.5; comprehensiveness at 7.0/10 ± 0.6; objectivity at 7.5/10 ± 0.4; safety at 7.5/10 ± 0.4; communication clarity at 7.3/10 ± 0.2; and acknowledgment of limitations at 7.7/10 ± 0.2. With regards to performance on procedures examined, the model's overall score was 7.0/10 ± 0.8 for breast augmentation; 7.6/10 ± 0.5 for mastopexy; and 7.4/10 ± 0.5 for breast reduction. The score on breast implant-specific knowledge was 6.7/10 ± 0.6. Conclusions: Albeit not without limitations, AI LLMs represent promising resources for patient guidance and patient education. The technology's machine learning capabilities may explain its improved performance efficiency.

Breast Implant Rupture Surveillance Practices among Plastic Surgeons in Publicly Funded Healthcare Systems.

BACKGROUND: The United States Food and Drug Administration (FDA) recommends silicone breast implant rupture surveillance using MRI or ultrasound beginning at 5-6 years post-insertion and every 2-3 years thereafter. No surveillance guidelines exist in Canada or the UK. This study aims to identify surveillance practices and perceived barriers to surveillance among plastic surgeons in Canada and the UK and compare surgeon practices between public and private sectors. METHODS: All active members of the Canadian Society of Plastic Surgeons (CSPS) and British Association of Plastic, Reconstructive and Aesthetic Surgeons (BAPRAS) with an interest in breast surgery were surveyed. Surgeon demographics, screening practices and perceived barriers to screening were analyzed. Fisher's exact test was used to detect significant associations. RESULTS: The survey response rate was 16%. Most surgeons (68%, n=58/85) were familiar with FDA guidelines; however, only 2.4% (n=2/85) followed them. Surgeons in private practice screened significantly more than surgeons in the public sector [45% (n=13/29) vs 13% (n=7/56), p<0.002]. Among surgeons, ultrasound and high-definition ultrasound were the most common screening modalities and a perceived lack of clinical necessity followed by a perceived lack of satisfactory evidence qualifying these guidelines was the most reported barrier to screening. CONCLUSION: Despite awareness of FDA guidelines, compliance among surgeons surveyed remains low, reportedly due to a perceived lack of necessity or evidence base. Most public sector surgeons opt not to screen and do not recommend surveillance, even without barriers. While governing bodies should consider current practices to shape guidelines, increasing surgeons' awareness on their evidentiary foundation and potential consequences of silent ruptures is imperative. 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.

Comparative Performance of Current Patient-Accessible Artificial Intelligence Large Language Models in the Preoperative Education of Patients in Facial Aesthetic Surgery.

Background: Artificial intelligence large language models (LLMs) represent promising resources for patient guidance and education in aesthetic surgery. Objectives: The present study directly compares the performance of OpenAI's ChatGPT (San Francisco, CA) with Google's Bard (Mountain View, CA) in this patient-related clinical application. Methods: Standardized questions were generated and posed to ChatGPT and Bard from the perspective of simulated patients interested in facelift, rhinoplasty, and brow lift. Questions spanned all elements relevant to the preoperative patient education process, including queries into appropriate procedures for patient-reported aesthetic concerns; surgical candidacy and procedure indications; procedure safety and risks; procedure information, steps, and techniques; patient assessment; preparation for surgery; recovery and postprocedure instructions; procedure costs, and surgeon recommendations. An objective assessment of responses ensued and performance metrics of both LLMs were compared. Results: ChatGPT scored 8.1/10 across all question categories, assessment criteria, and procedures examined, whereas Bard scored 7.4/10. Overall accuracy of information was scored at 6.7/10 ± 3.5 for ChatGPT and 6.5/10 ± 2.3 for Bard; comprehensiveness was scored as 6.6/10 ± 3.5 vs 6.3/10 ± 2.6; objectivity as 8.2/10 ± 1.0 vs 7.2/10 ± 0.8, safety as 8.8/10 ± 0.4 vs 7.8/10 ± 0.7, communication clarity as 9.3/10 ± 0.6 vs 8.5/10 ± 0.3, and acknowledgment of limitations as 8.9/10 ± 0.2 vs 8.1/10 ± 0.5, respectively. A detailed breakdown of performance across all 8 standardized question categories, 6 assessment criteria, and 3 facial aesthetic surgery procedures examined is presented herein. Conclusions: ChatGPT outperformed Bard in all assessment categories examined, with more accurate, comprehensive, objective, safe, and clear responses provided. Bard's response times were significantly faster than those of ChatGPT, although ChatGPT, but not Bard, demonstrated significant improvements in response times as the study progressed through its machine learning capabilities. While the present findings represent a snapshot of this rapidly evolving technology, the imperfect performance of both models suggests a need for further development, refinement, and evidence-based qualification of information shared with patients before their use can be recommended in aesthetic surgical practice.

Level of evidence in high impact surgical literature: the way forward.

Evidence-based medicine stipulates that clinical decision-making should revolve around scientific evidence. The goal of the present study is to evaluate the methodological quality of surgical research recently published in JAMA Surgery, International Journal of Surgery, and British Journal of Surgery, the three surgical journals with the highest impact factor. An electronic search of the PUBMED database was performed to retrieve all articles published in the JAMA Surgery, International Journal of Surgery, and British Journal of Surgery in the year 2022. Three authors independently reviewed all retrieved articles and methodological designs of the publications were analyzed and rated using a modification of the Oxford Centre for Evidence-Based Medicine Levels of Evidence (Oxford Levels of Evidence scale). The initial search identified 1236 articles of which 809 were excluded after title and abstract screening. The remaining 427 underwent full text/methods read, of which 164 did not meet the inclusion/exclusion criteria. A total of 273 studies were included in the analysis. The average level of evidence was 2.5 ± 0.8 across all studies assessed. The majority of study designs were comprised of retrospective cohorts (n = 119), prospective cohorts (n = 47), systematic reviews of non RCTs (n = 39), and RCTs (n = 37). There was no significant difference in the average level of evidence between the top three journals (p = 0.50). Most clinical studies in the highest impact factor surgical journals are of level III evidence, consistent with earlier literature. However, our analysis demonstrates a relatively higher percentage of LOE I and II compared to what was previously published in the literature.

Intraoperative Systolic Blood Pressure as a Significant Predictor of Postoperative Hematoma Following Facelift: Single Surgeon Experience of 118 Consecutive Facelifts.

BACKGROUND: While there exists ample evidence on the impact of perioperative blood pressure on hematoma incidence following facelift; the association of elevated or labile intraoperative blood pressure with postoperative hematoma remains to be explored. OBJECTIVES: The authors evaluate the association of elevated or labile intraoperative systolic blood pressure (SBP) with postoperative hematoma, using the senior author's single surgeon experience of 118 consecutive facelifts. METHODS: A multivariate logistic regression was conducted using complete demographic, procedure-related, blood pressure-related, and outcomes-related data, with the outcome of interest representing postoperative hematoma. One-way ANOVA and linear regression analyses were used to assess for significant associations between a preoperative history of hypertension and a tendency to demonstrate elevated or labile intraoperative SBP. A Fisher's Exact test was subsequently used to assess for specific intraoperative SBP measurement cut-offs significantly associated with postoperative hematoma, including maximum recorded intraoperative SBP, and specific degree of intraoperative SBP fluctuations. RESULTS: Multivariate logistic regression demonstrated no statistically significant patient- or procedure-related demographic predictors of postoperative hematoma. With aggressive treatment of preoperative hypertension, high preoperative SBP was not found to be a significant predictor of postoperative hematoma following facelift, although this approached statistical significance (p=0.05). In contrast, labile intraoperative SBP (maximum recorded intraoperative SBP - minimum recorded intraoperative SBP; p=0.026), as well as high immediate postoperative SBP (p=0.002), were both independent and statistically significant predictors of postoperative hematoma. Patients with a preoperative history of hypertension, and more specifically those with elevated SBP measurements in the preoperative clinic, were more likely to demonstrate labile (p=0.007) or elevated (p=0.005) intraoperative SBP during facelift surgery. Specifically, maximum recorded intraoperative SBP ≥155mmHg (p=0.045), as well as maximum intraoperative SBP fluctuations ≥80mmHg (p=0.036) were found to be significantly associated with hematoma. CONCLUSIONS: A multimodal approach is necessary to control perioperative systolic blood pressure within the strict <120mmHg target demonstrated to significantly decrease hematoma incidence. The senior author's preference is for intraoperative SBP to remain within a strict 90-100mmHg range. In contrast to hypertension that is aggressively treated and successfully controlled, hypertension that is difficult to control intraoperatively, may be a predictor of systolic blood pressure that is difficult to control postoperatively, and thus a significant risk factor for postoperative hematoma following facelift.

Successful Applicant and Program Director Perspectives on the Virtual Residency Selection Process for Canadian Surgical Subspecialties.

Introduction: The COVID-19 pandemic imparted an important shift in strategies postgraduate surgical programs use to recruit, interact with, and select medical students applying through the Canadian Resident Matching Service (CaRMS). With this unprecedented shift toward virtual applicant selection, this study sought to explore and analyze perspectives of the first cohort of program directors (PDs) and applicants who participated in this process. Methods: A cross-sectional survey study was designed using Google Forms for both PDs and applicants participating in the 2021 CaRMS surgical subspecialty selection process. Questions pertained to format and content of virtual engagement methods, the interview itself, as well as advice for future applicants. Results: Thirty-five PDs and 40 successful applicants (n = 75) participated in the study. Cost reduction was the most commonly reported benefit of online interviewing by PDs (85%), followed by efficiency (71%), enhanced resource management (49%), and ability to conduct more interviews (23%). Strong letters of reference (80%) and interview performance (74%) remained the most significant factors in virtual applicant selection. Attendance to virtual recruitment events did not increase the likelihood of offering interviews (n = 24, 69% of PDs), although the ability to perform in-person electives held tremendous value. Most applicants (90%) reported on virtual information sessions as the best method for learning about programs; work culture and environment were topics most valued as discussion points (90%). Successful applicants provided an average confidence of 76% regarding their suitability with their matched programs. Seventy-three percent of applicants (n = 29) had either a preference for virtual interviews or were equivocal, while 51.4% of PDs (n = 18) preferred interviews to be conducted virtually for future cohorts. Conclusion: Trainees are entering residency with confidence following a virtual selection process, and PDs feel confident in their selections. Although no clear consensus exists regarding preference for virtual or in-person interviews, several advantages for virtual resident selection exist. The influence of an in-person elective was found difficult to replace, regardless of interview format. The importance of applicant engagement with programs prior to interviews is highlighted and discussed with recommendations provided for best practices.

Introduction: La pandémie de COVID-19 a induit un important changement de stratégies de l'utilisation des programmes chirurgicaux de 3e cycle pour recruter, interagir avec les étudiants en médecine et sélectionner les étudiants déposant une candidature par le biais du CaRMS, le service d'affectation des résidents canadiens. Avec ce mouvement sans précédent vers une sélection virtuelle des candidats, l'étude a cherché à explorer et analyser les points de vue de la première cohorte de directeurs de programmes (DP) et de candidats ayant participé à ce processus. Méthodes: Une enquête transversale a été conçue à l'aide de Google Forms pour, à la fois, les DP et les candidats participant au processus de sélection de sous-spécialité chirurgicale 2021 du CaRMS. Des questions portaient sur le format et le contenu des méthodes de contact virtuelles, l'entretien proprement dit ainsi que sur des conseils pour les candidats futurs. Résultats: Trente-cinq DP et quarante candidats acceptés (n = 75) ont participé à l'étude. La réduction des coûts a été l'avantage des entretiens en ligne le plus souvent cité par les DP (85 %) suivie de l'efficacité (71 %), d'une meilleure gestion des ressources (49 %) et de la capacité à réaliser plus d'entretiens (23 %). De solides lettres de recommandation (80 %) et la performance au cours de l'entretien (74 %) restaient les facteurs les plus significatifs pour la sélection virtuelle des candidats. La participation aux événements virtuels de recrutement n'augmentait pas la probabilité d'offre d'un entretien (n = 24, 69 % des DP), même si la possibilité de réaliser des entretiens facultatifs en personne avait une valeur considérable. La plupart des candidats (90 %) ont indiqué que les séances d'information virtuelles étaient la meilleure méthode pour en savoir plus sur les programmes; la culture et l'environnement de travail étaient les thèmes ayant le plus de valeur comme sujets de discussion (90 %). Les candidats ayant réussi ont indiqué à 76 % qu'ils s'estimaient confiants sur le fait de bien correspondre aux programmes auxquels ils étaient affectés. Soixante-treize pour cent des candidats (n = 29) avaient une préférence pour les entretiens virtuels ou étaient dans le doute, tandis que 51,4 % des DP (n = 18) préféraient que les entretiens des cohortes futures soient menés virtuellement. Conclusion: Les stagiaires commencent leur résidence avec confiance après un processus de sélection virtuel et les DP se sentent confiants quant à leurs choix. Même s'il n'existe pas un franc consensus concernant une préférence pour les entretiens virtuels ou en face à face, la sélection à distance des résidents présente plusieurs avantages. L'influence d'un entretien optionnel en personne s'est avérée difficile à remplacer, quelle que soit la forme donnée à l'entretien. L'importance de l'engagement du candidat dans des programmes antérieurs aux entretiens est soulignée et discutée avec des recommandations fournies pour de meilleures pratiques.

Complications of Aesthetic and Reconstructive Breast Implant Capsulectomy: An Analysis of 7486 Patients Using Nationwide Outcomes Data.

BACKGROUND: Despite increasing demand for breast capsular surgery to treat various benign and malignant implant-related pathologies, high-quality evidence elucidating complication profiles of capsulectomy and capsulotomy is lacking. OBJECTIVES: The aim of this study was to provide the largest-scale analysis of associated outcomes and complications using the Tracking Operations and Outcomes for Plastic Surgeons (TOPS) database, and to investigate clinical scenarios that may subject patients to increased risks for complications, most notably extent of capsular surgery (complete vs partial) and index indication of implantation (aesthetic vs reconstructive). METHODS: An analysis of the TOPS database from 2008 to 2019 was performed. CPT codes were used to identify complete capsulectomy and partial capsulectomy/capsulotomy cases. Breast implant exchange procedures constituted procedural controls. RESULTS: In total, 7486 patients (10,703 breasts) undergoing capsulectomy or capsulotomy were assessed. Relative to controls, capsulectomy (4.40% vs 5.79%), but not capsulotomy (4.40% vs 4.50%), demonstrated higher overall complication rates. Both capsulectomies (0.83% vs 0.23%) and capsulotomies (0.56% vs 0.23%) also had greater rates of seroma relative to controls. Subgroup analyses demonstrated that reconstructive patients, relative to aesthetic patients, experienced greater overall complications (6.76% vs 4.34%), and increased risks for seroma (1.06% vs 0.47%), dehiscence (0.46% vs 0.14%), surgical site infections (1.03% vs 0.23%), and implant loss (0.52% vs 0.23%). A detailed synthesis of 30-day outcomes, including all patient- and breast-specific complications, for both capsulectomy and capsulotomy, stratified according to all potential confounders, is presented herein. CONCLUSIONS: Surgeries on the breast capsule are safe overall, although complete capsulectomies and reconstructive patients are associated with significantly increased operative risks. The present findings will enhance patient selection, counseling, and informed consent.

Complications of Aesthetic Liposuction Performed in Isolation: A Systematic Literature Review and Meta-Analysis.

Introduction: Aesthetic liposuction represents one of the most commonly performed cosmetic procedures worldwide. The purpose of this article is to examine and synthesize reported complication rates and explore the analytical prospect of possible patient or procedure-related predictive factors associated with specific complications. Methods: A systematic review was performed using the Pubmed, Cochrane, and Embase databases in line with specific criteria set to ensure an accurate assessment of complication rates; extracted data was synthesized through a random-effects model and meta-analysis of proportions. Results: A total of 60 studies were included in the meta-analysis, representing 21,776 patients undergoing aesthetic liposuction. Most studies followed an observational design. The overall complication rate was 12% (95% confidence interval [CI] 8%, 16%). When stratifying according to specific complications, the incidence of contour irregularities was determined to be 2% (95% CI 1%, 2%), seroma 2% (95% CI 1%; 2%), hematoma 1% (95% CI 0%, 1%), surgical site infection 1% (95% CI 1%, 2%), fibrosis or induration 1% (95% CI 1%, 2%), and pigmentary changes 1% (95% CI 1%, 1%), among others. A meta-regression to identify patient- or procedure-related factors associated with greater complication rates proved infeasible given the nature of the available data. Conclusion: Overall, liposuction demonstrated a relatively low complication rate profile, however, a considerable degree of heterogeneity exists within the examined literature preventing the recognition of predictive risk factors. While this calls for efforts to establish consensus on unified methods of outcomes reporting, the present meta-analysis can serve to provide practitioners with an evidence-based reference to improve informed consent and inform clinical guidelines, specifically pertaining to the incidence of commonly encountered complications in aesthetic liposuction, of which presently available survey studies and database queries remain devoid.

Introduction : La liposuccion esthétique est l'une des procédures esthétiques le plus souvent réalisées dans le monde. L'objectif de cet article est d'étudier et synthétiser les taux de complications rapportés et d'explorer la possibilité d'analyse de possibles facteurs prédictifs liés aux patients ou à la procédure en association avec des complications spécifiques. Méthodes : Une revue systématique a été exécutée à partir des bases de données Pubmed, Cochrane et Embase selon un ensemble de critères spécifiques pour assurer une évaluation précise des taux de complications; les données extraites ont été synthétisées par un modèle d'effets aléatoires et une méta-analyse des pourcentages. Résultats : Un total de 60 études a été inclus dans la méta-analyse, représentant 21 776 patients subissant une liposuccion esthétique. La plupart des études étaient observationnelles. Le taux global de complications était de 12% (IC à 95% : 8% à 16%). Après stratification selon des complications spécifiques, les incidences suivantes ­ parmi d'autres ­ ont été établies : irrégularités de contour a été établi à 2% (IC à 95% : 1% à 2%), sérome 2% (IC à 95% : 1% à 2%), hématome 1% (IC à 95% : 0% à 1%), infection du site opératoire 1% (IC à 95% : 1% à 2%), fibrose ou induration 1% (IC à 95% : 1% à 2%) et modification de la pigmentation 1% (IC à 95% : 1% à 1%). Une méta-régression visant à identifier des facteurs liés aux patients ou à la procédure pour les taux de complications les plus élevés s'est avérée infaisable, compte tenu de la nature des données disponibles. Conclusion : Globalement, la liposuccion a montré un relativement bas profil en termes de taux de complications. Il existe cependant une hétérogénéité considérable dans les publications étudiées, empêchant d'identifier des facteurs de risque prédictifs. Cela appelle à des efforts en vue de l'établissement d'un consensus sur des méthodes uniformisées de déclaration des résultats, mais la présente méta-analyse peut permettre aux praticiens de disposer d'une référence basée sur des constatations probantes pour améliorer le consentement éclairé et enrichir les lignes directrices cliniques, en particulier pour ce qui concerne l'incidence des complications fréquemment vues dans la liposuccion esthétique. En effet, les études, enquêtes et bases de données actuellement disponibles en sont dépourvues.

Artificial Intelligence-Generated Social Media Content Creation and Management Strategies for Plastic Surgeons.

BACKGROUND: Social media platforms have come to represent integral components of the professional marketing and advertising strategy for plastic surgeons. Effective and consistent content development, however, remains technically demanding and time consuming, prompting most to employ, at non-negligible costs, social media marketing specialists for content planning and development. OBJECTIVES: In the present study, we aimed to investigate the ability of presently available artificial intelligence (AI) models to assist plastic surgeons in their social media content development and sharing plans. METHODS: An AI large language model was prompted on the study's objectives through a series of standardized user interactions. Social media platforms of interest, on which the AI model was prompted, included Instagram, TikTok, and X (formerly Twitter). RESULTS: A 1-year, entirely AI-generated social media plan, comprising a total of 1091 posts for the 3 aforementioned social media platforms, is presented. Themes of the AI-generated content proposed for each platform were classified in 6 categories, including patient-related, practice-related, educational, "uplifting," interactive, and promotional posts. Overall, 91 publicly recognized holidays and observant and awareness days were incorporated into the content calendars. The AI model demonstrated an ability to differentiate between the distinct formats of each of the 3 social media platforms investigated, generating unique ideas for each, and providing detailed content development and posting instructions, scripts, and post captions, leveraging features specific to each platform. CONCLUSIONS: By providing detailed and actionable social media content creation and posting plans to plastic surgeons, presently available AI models can be readily leveraged to assist in and significantly alleviate the burden associated with social media account management, content generation, and potentially patient conversion.

Utility and Comparative Performance of Current Artificial Intelligence Large Language Models as Postoperative Medical Support Chatbots in Aesthetic Surgery.

BACKGROUND: Large language models (LLMs) have revolutionized the way plastic surgeons and their patients can access and leverage artificial intelligence (AI). OBJECTIVES: The present study aims to compare the performance of 2 current publicly available and patient-accessible LLMs in the potential application of AI as postoperative medical support chatbots in an aesthetic surgeon's practice. METHODS: Twenty-two simulated postoperative patient presentations following aesthetic breast plastic surgery were devised and expert-validated. Complications varied in their latency within the postoperative period, as well as urgency of required medical attention. In response to each patient-reported presentation, Open AI's ChatGPT and Google's Bard, in their unmodified and freely available versions, were objectively assessed for their comparative accuracy in generating an appropriate differential diagnosis, most-likely diagnosis, suggested medical disposition, treatments or interventions to begin from home, and/or red flag signs/symptoms indicating deterioration. RESULTS: ChatGPT cumulatively and significantly outperformed Bard across all objective assessment metrics examined (66% vs 55%, respectively; P < .05). Accuracy in generating an appropriate differential diagnosis was 61% for ChatGPT vs 57% for Bard (P = .45). ChatGPT asked an average of 9.2 questions on history vs Bard's 6.8 questions (P < .001), with accuracies of 91% vs 68% reporting the most-likely diagnosis, respectively (P < .01). Appropriate medical dispositions were suggested with accuracies of 50% by ChatGPT vs 41% by Bard (P = .40); appropriate home interventions/treatments with accuracies of 59% vs 55% (P = .94), and red flag signs/symptoms with accuracies of 79% vs 54% (P < .01), respectively. Detailed and comparative performance breakdowns according to complication latency and urgency are presented. CONCLUSIONS: ChatGPT represents the superior LLM for the potential application of AI technology in postoperative medical support chatbots. Imperfect performance and limitations discussed may guide the necessary refinement to facilitate adoption.

Clinical, Radiologic, and Functional Outcomes following Bone Grafting for Metacarpal Non-Unions: A Systematic Review.

Objectives: Metacarpal non-unions are complex hand defects that can lead to severe hand impairment. Treatment may require the use of artificial or autologous bone grafts. This systematic review aims to describe the outcomes of bone grafting following metacarpal non-union in an attempt to establish an optimal therapeutic protocol for this complication. Methods: A systematic review was conducted in adherence with PRISMA guidelines. Data collection and analysis were performed in duplicate and confirmed by a third investigator. Our primary outcomes focused on radiological time to bone fusion and rates of non-union. Additionally, functional outcomes and complications were analyzed as means of central tendency. Results: Eighteen studies were included in the systematic review, accounting for a total of 47 patients. The average follow-up time was 12.4 months. Fourteen studies analyzed radiological outcomes, with atrophic non-union representing the most common type. The time to bone fusion, assessed radiologically, following bone graft was an average of 6.9 months (n = 14), with a 100% rate of union in 42 patients. Regarding patient-reported pain improvement, 76% of patients experienced pain relief. Moreover, all patients reported a complete subjective return to baseline hand function. Adverse events, limited to hematoma and seroma, were seen in three patients, representing a complication rate of 11.8% in the examined population. Conclusions: Metacarpal non-union can be treated successfully via vascularized and non-vascularized bone grafting. Based on the available evidence, bone grafts demonstrate favorable union rates, post-operative pain reduction, hand function recovery, earlier bone fusion times, and minimal complications in the context of metacarpal non-union management.

Complications Following Body Contouring: Performance Validation of Bard, a Novel AI Large Language Model, in Triaging and Managing Postoperative Patient Concerns.

INTRODUCTION: Large language models (LLM) have revolutionized the way humans interact with artificial intelligence (AI) technology, with marked potential for applications in esthetic surgery. The present study evaluates the performance of Bard, a novel LLM, in identifying and managing postoperative patient concerns for complications following body contouring surgery. METHODS: The American Society of Plastic Surgeons' website was queried to identify and simulate all potential postoperative complications following body contouring across different acuities and severity. Bard's accuracy was assessed in providing a differential diagnosis, soliciting a history, suggesting a most-likely diagnosis, appropriate disposition, treatments/interventions to begin from home, and red-flag signs/symptoms indicating deterioration, or requiring urgent emergency department (ED) presentation. RESULTS: Twenty-two simulated body contouring complications were examined. Overall, Bard demonstrated a 59% accuracy in listing relevant diagnoses on its differentials, with a 52% incidence of incorrect or misleading diagnoses. Following history-taking, Bard demonstrated an overall accuracy of 44% in identifying the most-likely diagnosis, and a 55% accuracy in suggesting the indicated medical dispositions. Helpful treatments/interventions to begin from home were suggested with a 40% accuracy, whereas red-flag signs/symptoms, indicating deterioration, were shared with a 48% accuracy. A detailed analysis of performance, stratified according to latency of postoperative presentation (<48hours, 48hours-1month, or >1month postoperatively), and according to acuity and indicated medical disposition, is presented herein. CONCLUSIONS: Despite promising potential of LLMs and AI in healthcare-related applications, Bard's performance in the present study significantly falls short of accepted clinical standards, thus indicating a need for further research and development prior to adoption. LEVEL OF EVIDENCE IV: 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 .

Large Language Models and Artificial Intelligence: A Primer for Plastic Surgeons on the Demonstrated and Potential Applications, Promises, and Limitations of ChatGPT.

BACKGROUND: The rapidly evolving field of artificial intelligence (AI) holds great potential for plastic surgeons. ChatGPT, a recently released AI large language model (LLM), promises applications across many disciplines, including healthcare. OBJECTIVES: The aim of this article was to provide a primer for plastic surgeons on AI, LLM, and ChatGPT, including an analysis of current demonstrated and proposed clinical applications. METHODS: A systematic review was performed identifying medical and surgical literature on ChatGPT's proposed clinical applications. Variables assessed included applications investigated, command tasks provided, user input information, AI-emulated human skills, output validation, and reported limitations. RESULTS: The analysis included 175 articles reporting on 13 plastic surgery applications and 116 additional clinical applications, categorized by field and purpose. Thirty-four applications within plastic surgery are thus proposed, with relevance to different target audiences, including attending plastic surgeons (n = 17, 50%), trainees/educators (n = 8, 24.0%), researchers/scholars (n = 7, 21%), and patients (n = 2, 6%). The 15 identified limitations of ChatGPT were categorized by training data, algorithm, and ethical considerations. CONCLUSIONS: Widespread use of ChatGPT in plastic surgery will depend on rigorous research of proposed applications to validate performance and address limitations. This systemic review aims to guide research, development, and regulation to safely adopt AI in plastic surgery.

Complications Following Facelift and Neck Lift: Implementation and Assessment of Large Language Model and Artificial Intelligence (ChatGPT) Performance Across 16 Simulated Patient Presentations.

INTRODUCTION: ChatGPT represents a potential resource for patient guidance and education, with the possibility for quality improvement in healthcare delivery. The present study evaluates the role of ChatGPT as an interactive patient resource, and assesses its performance in identifying, triaging, and guiding patients with concerns of postoperative complications following facelift and neck lift surgery. METHODS: Sixteen patient profiles were generated to simulate postoperative patient presentations, with complications of varying acuity and severity. ChatGPT was assessed for its accuracy in generating a differential diagnosis, soliciting a history, providing the most-likely diagnosis, the appropriate disposition, treatments/interventions to begin from home, and red-flag symptoms necessitating an urgent presentation to the emergency department. RESULTS: Overall accuracy in providing a complete differential diagnosis in response to simulated presentations was 85%, with an accuracy of 88% in identifying the most-likely diagnosis after history-taking. However, appropriate patient dispositions were suggested in only 56% of cases. Relevant home treatments/interventions were suggested with an 82% accuracy, and red-flag symptoms with a 73% accuracy. A detailed analysis, stratified according to latency of postoperative presentation (<48 h, 48 h-1 week, or >1 week), and according to acuity of complications, is presented herein. CONCLUSIONS: ChatGPT overestimated the urgency of indicated patient dispositions in 44% of cases, concerning for potential unnecessary increase in healthcare resource utilization. Imperfect performance, and the tool's tendency for overinclusion in its responses, risk increasing patient anxiety and straining physician-patient relationships. While artificial intelligence has great potential in triaging postoperative patient concerns, and improving efficiency and resource utilization, ChatGPT's performance, in its current form, demonstrates a need for further refinement before its safe and effective implementation in facial aesthetic surgical practice. LEVEL OF EVIDENCE IV: 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 .

Systolic Blood Pressure Less Than 120 mmHg is a Safe and Effective Method to Minimize Bleeding After Facelift Surgery: A Review of 502 Consecutive Cases.

BACKGROUND: Hematoma is the most common complication after facelift surgery. Hypertension is the major risk factor for hematoma following facelift. Measures taken to reduce systolic blood pressure perioperatively significantly reduce the risk of hematoma. There is evidence that treating systolic blood pressure of 140 mmHg or above reduces hematoma; there were no studies to date in which systolic blood pressures below 120 mmHg had been evaluated. OBJECTIVES: To assess the safety and efficacy of maintaining systolic blood pressures of 120 mmHg or less postoperatively to reduce hematoma after facelift. METHODS: A retrospective chart review of a single surgeon's series of facelift procedures from January 2004 to July 2018 was undertaken. Implementation of a more stringent perioperative blood pressure protocol (maintaining a systolic blood pressure of less than 120 mmHg postoperatively) was initiated in January of 2013, dividing patients into 2 groups. RESULTS: A total of 502 consecutive patients who underwent a facelift by F.N. were included in the study. A total of 319 patients underwent a facelift before 2013, and a total of 183 patients underwent a facelift in 2013 or later. Overall, a total of 13 hematomas occurred during the entire 15-year study period (2.59%), of which 12 occurred before the implementation of a strict blood pressure regimen (3.76%), and only 1 occurred after the new protocol (0.5%). There were no adverse events related to the lower blood pressure. CONCLUSIONS: Treating systolic blood pressure greater than 120 mmHg postoperatively is a safe and effective method for reducing the risk of hematoma after facelift.