Background: Surgeons have historically used age as a preoperative predictor of postoperative outcomes. Sarcopenia, the loss of skeletal muscle mass due to disease or biological age, has been proposed as a more accurate risk predictor. The prognostic value of sarcopenia assessment in surgical patients remains poorly understood. Therefore, the authors aimed to synthesize the available literature and investigate the impact of sarcopenia on perioperative and postoperative outcomes across all surgical specialties. Methods: The authors systematically assessed the prognostic value of sarcopenia on postoperative outcomes by conducting a systematic review and meta-analysis according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching the PubMed/MEDLINE and EMBASE databases from inception to 1st October 2022. Their primary outcomes were complication occurrence, mortality, length of operation and hospital stay, discharge to home, and postdischarge survival rate at 1, 3, and 5 years. Subgroup analysis was performed by stratifying complications according to the Clavien–Dindo classification system. Sensitivity analysis was performed by focusing on studies with an oncological, cardiovascular, emergency, or transplant surgery population and on those of higher quality or prospective study design. Results: A total of 294 studies comprising 97 643 patients, of which 33 070 had sarcopenia, were included in our analysis. Sarcopenia was associated with significantly poorer postoperative outcomes, including greater mortality, complication occurrence, length of hospital stay, and lower rates of discharge to home (all P<0.00001). A significantly lower survival rate in patients with sarcopenia was noted at 1, 3, and 5 years (all P<0.00001) after surgery. Subgroup analysis confirmed higher rates of complications and mortality in oncological (both P<0.00001), cardiovascular (both P<0.00001), and emergency (P=0.03 and P=0.04, respectively) patients with sarcopenia. In the transplant surgery cohort, mortality was significantly higher in patients with sarcopenia (P<0.00001). Among all patients undergoing surgery for inflammatory bowel disease, the frequency of complications was significantly increased among sarcopenic patients (P=0.007). Sensitivity analysis based on higher quality studies and prospective studies showed that sarcopenia remained a significant predictor of mortality and complication occurrence (all P<0.00001). Conclusion: Sarcopenia is a significant predictor of poorer outcomes in surgical patients. Preoperative assessment of sarcopenia can help surgeons identify patients at risk, critically balance eligibility, and refine perioperative management. Large-scale studies are required to further validate the importance of sarcopenia as a prognostic indicator of perioperative risk, especially in surgical subspecialties.

As the body’s integumentary system, the skin is vulnerable to injuries. The subsequent wound healing processes aim to restore dermal and epidermal integrity and functionality. To this end, multiple tissue-resident cells and recruited immune cells cooperate to efficiently repair the injured tissue. Such temporally- and spatially-coordinated interplay necessitates tight regulation to prevent collateral damage such as overshooting immune responses and excessive inflammation. In this context, regulatory T cells (Tregs) hold a key role in balancing immune homeostasis and mediating cutaneous wound healing. A comprehensive understanding of Tregs’ multifaceted field of activity may help decipher wound pathologies and, ultimately, establish new treatment modalities. Herein, we review the role of Tregs in orchestrating the regeneration of skin adnexa and catalyzing healthy wound repair. Further, we discuss how Tregs operate during fibrosis, keloidosis, and scarring.

Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) “OMFS” OR “oral and maxillofacial” OR “oral and maxillofacial surgery” OR “oral surgery” AND (2) “AI” OR “artificial intelligence”. The search format was tailored to each database’s syntax. To find pertinent material, each retrieved article and systematic review’s reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.

(1) Background: As the field of artificial intelligence (AI) evolves, tools like ChatGPT are increasingly integrated into various domains of medicine, including medical education and research. Given the critical nature of medicine, it is of paramount importance that AI tools offer a high degree of reliability in the information they provide. (2) Methods: A total of n = 450 medical examination questions were manually entered into ChatGPT thrice, each for ChatGPT 3.5 and ChatGPT 4. The responses were collected, and their accuracy and consistency were statistically analyzed throughout the series of entries. (3) Results: ChatGPT 4 displayed a statistically significantly improved accuracy with 85.7% compared to that of 57.7% of ChatGPT 3.5 (p < 0.001). Furthermore, ChatGPT 4 was more consistent, correctly answering 77.8% across all rounds, a significant increase from the 44.9% observed from ChatGPT 3.5 (p < 0.001). (4) Conclusions: The findings underscore the increased accuracy and dependability of ChatGPT 4 in the context of medical education and potential clinical decision making. Nonetheless, the research emphasizes the indispensable nature of human-delivered healthcare and the vital role of continuous assessment in leveraging AI in medicine.

Free tissue transfer is widely used for the reconstruction of complex tissue defects. The survival of free flaps depends on the patency and integrity of the microvascular anastomosis. Accordingly, the early detection of vascular comprise and prompt intervention are indispensable to increase flap survival rates. Such monitoring strategies are commonly integrated into the perioperative algorithm, with clinical examination still being considered the gold standard for routine free flap monitoring. Despite its widespread acceptance as state of the art, the clinical examination also has its pitfalls, such as the limited applicability in buried flaps and the risk of poor interrater agreement due to inconsistent flap (failure) appearances. To compensate for these shortcomings, a plethora of alternative monitoring tools have been proposed in recent years, each of them with inherent strengths and limitations. Given the ongoing demographic change, the number of older patients requiring free flap reconstruction, e.g., after cancer resection, is rising. Yet, age-related morphologic changes may complicate the free flap evaluation in elderly patients and delay the prompt detection of clinical signs of flap compromise. In this review, we provide an overview of currently available and employed methods for free flap monitoring, with a special focus on elderly patients and how senescence may impact standard free flap monitoring strategies.

Background: The United States Medical Licensing Examination (USMLE) has been critical in medical education since 1992, testing various aspects of a medical student’s knowledge and skills through different steps, based on their training level. Artificial intelligence (AI) tools, including chatbots like ChatGPT, are emerging technologies with potential applications in medicine. However, comprehensive studies analyzing ChatGPT’s performance on USMLE Step 3 in large-scale scenarios and comparing different versions of ChatGPT are limited. Objective: This paper aimed to analyze ChatGPT’s performance on USMLE Step 3 practice test questions to better elucidate the strengths and weaknesses of AI use in medical education and deduce evidence-based strategies to counteract AI cheating. Methods: A total of 2069 USMLE Step 3 practice questions were extracted from the AMBOSS study platform. After including 229 image-based questions, a total of 1840 text-based questions were further categorized and entered into ChatGPT 3.5, while a subset of 229 questions were entered into ChatGPT 4. Responses were recorded, and the accuracy of ChatGPT answers as well as its performance in different test question categories and for different difficulty levels were compared between both versions. Results: Overall, ChatGPT 4 demonstrated a statistically significant superior performance compared to ChatGPT 3.5, achieving an accuracy of 84.7% (194/229) and 56.9% (1047/1840), respectively. A noteworthy correlation was observed between the length of test questions and the performance of ChatGPT 3.5 (ρ=–0.069; P=.003), which was absent in ChatGPT 4 (P=.87). Additionally, the difficulty of test questions, as categorized by AMBOSS hammer ratings, showed a statistically significant correlation with performance for both ChatGPT versions, with ρ=–0.289 for ChatGPT 3.5 and ρ=–0.344 for ChatGPT 4. ChatGPT 4 surpassed ChatGPT 3.5 in all levels of test question difficulty, except for the 2 highest difficulty tiers (4 and 5 hammers), where statistical significance was not reached. Conclusions: In this study, ChatGPT 4 demonstrated remarkable proficiency in taking the USMLE Step 3, with an accuracy rate of 84.7% (194/229), outshining ChatGPT 3.5 with an accuracy rate of 56.9% (1047/1840). Although ChatGPT 4 performed exceptionally, it encountered difficulties in questions requiring the application of theoretical concepts, particularly in cardiology and neurology. These insights are pivotal for the development of examination strategies that are resilient to AI and underline the promising role of AI in the realm of medical education and diagnostics.