Purpose With the increasing adoption of artificial intelligence (AI) in various domains, including healthcare, there is growing acceptance and interest in consulting AI models to provide medical information and advice. This study aimed to evaluate the accuracy of ChatGPT’s responses to practice quiz questions designed for otolaryngology board certification and decipher potential performance disparities across different otolaryngology subspecialties. Methods A dataset covering 15 otolaryngology subspecialties was collected from an online learning platform funded by the German Society of Oto-Rhino-Laryngology, Head and Neck Surgery, designed for board certification examination preparation. These questions were entered into ChatGPT, with its responses being analyzed for accuracy and variance in performance. Results The dataset included 2576 questions (479 multiple-choice and 2097 single-choice), of which 57% (n = 1475) were answered correctly by ChatGPT. An in-depth analysis of question style revealed that single-choice questions were associated with a significantly higher rate (p < 0.001) of correct responses (n = 1313; 63%) compared to multiple-choice questions (n = 162; 34%). Stratified by question categories, ChatGPT yielded the highest rate of correct responses (n = 151; 72%) in the field of allergology, whereas 7 out of 10 questions (n = 65; 71%) on legal otolaryngology aspects were answered incorrectly. Conclusion The study reveals ChatGPT’s potential as a supplementary tool for otolaryngology board certification preparation. However, its propensity for errors in certain otolaryngology areas calls for further refinement. Future research should address these limitations to improve ChatGPT’s educational use. An approach, with expert collaboration, is recommended for the reliable and accurate integration of such AI models.

Purpose The use of AI-powered technology, particularly OpenAI’s ChatGPT, holds significant potential to reshape healthcare and medical education. Despite existing studies on the performance of ChatGPT in medical licensing examinations across different nations, a comprehensive, multinational analysis using rigorous methodology is currently lacking. Our study sought to address this gap by evaluating the performance of ChatGPT on six different national medical licensing exams and investigating the relationship between test question length and ChatGPT’s accuracy. Methods We manually inputted a total of 1,800 test questions (300 each from US, Italian, French, Spanish, UK, and Indian medical licensing examination) into ChatGPT, and recorded the accuracy of its responses. Results We found significant variance in ChatGPT’s test accuracy across different countries, with the highest accuracy seen in the Italian examination (73% correct answers) and the lowest in the French examination (22% correct answers). Interestingly, question length correlated with ChatGPT’s performance in the Italian and French state examinations only. In addition, the study revealed that questions requiring multiple correct answers, as seen in the French examination, posed a greater challenge to ChatGPT. Conclusion Our findings underscore the need for future research to further delineate ChatGPT’s strengths and limitations in medical test-taking across additional countries and to develop guidelines to prevent AI-assisted cheating in medical examinations.

Vascularized composite allotransplantation (VCA) is an evolving field of reconstructive surgery that has revolutionized the treatment of patients with devastating injuries, including those with limb losses or facial disfigurement. The transplanted units are typically comprised of different tissue types, including skin, mucosa, blood and lymphatic vasculature, muscle, and bone. It is widely accepted that the antigenicity of some VCA components, such as skin, is particularly potent in eliciting a strong recipient rejection response following transplantation. The fine line between tolerance and rejection of the graft is orchestrated by different cell types, including both donor and recipient-derived lymphocytes, macrophages, and other immune and donor-derived tissue cells (e.g., endothelium). Here, we delineate the role of different cell and tissue types during VCA rejection. Rejection of VCA grafts and the necessity of life-long multidrug immunosuppression remains one of the major challenges in this field. This review sheds light on recent developments in decoding the cellular signature of graft rejection in VCA and how these may, ultimately, influence the clinical management of VCA patients by way of novel therapies that target specific cellular processes.

Transplant rejection remains a challenge especially in the field of vascularized composite allotransplantation (VCA). To blunt the alloreactive immune response‚ stable levels of maintenance immunosupression are required. However‚ the need for lifelong immunosuppression poses the risk of severe side effects, such as increased risk of infection, metabolic complications, and malignancies. To balance therapeutic efficacy and medication side effects, immunotolerance promoting immune cells (especially regulatory T cells [Treg]) have become of great scientific interest. This approach leverages immune system mechanisms that usually ensure immunotolerance toward self-antigens and prevent autoimmunopathies. Treg can be bioengineered to express a chimeric antigen receptor or a T-cell receptor. Such bioengineered Treg can target specific antigens and thereby reduce unwanted off-target effects. Treg have demonstrated beneficial clinical effects in solid organ transplantation and promising in vivo data in VCAs. In this review, we summarize the functional, phenotypic, and immunometabolic characteristics of Treg and outline recent advancements and current developments regarding Treg in the field of VCA and solid organ transplantation.

Ongoing research has highlighted the significance of the cross-play of macrophages and mesenchymal stem cells (MSCs). Lysine-specific demethylase 6B (KDM6B) has been shown to control osteogenic differentiation of MSCs by depleting trimethylated histone 3 lysine 27 (H3K27me3). However, to date, the role of KDM6B in bone marrow-derived macrophages (BMDMs) remains controversial. Here, a chromatin immunoprecipitation assay (ChIP) proved that KDM6B derived from osteogenic-induced BMSCs could bind to the promoter region of BMDMs' brain and muscle aryl hydrocarbon receptor nuclear translocator-like protein-1 (BMAL1) gene in a coculture system and activate BMAL1. Transcriptome sequencing and experiments in vitro showed that the overexpression of BMAL1 in BMDM could inhibit the TLR2/NF-κB signaling pathway, reduce pyroptosis, and decrease the M1/M2 ratio, thereby promoting osteogenic differentiation of BMSCs. Furthermore, bone and macrophage dual-targeted GSK-J4 (KDM6B inhibitor)-loaded nanodiscs were synthesized via binding SDSSD-apoA-1 peptide analogs (APA) peptide, which indirectly proved the critical role of KDM6B in osteogenesis in vivo. Overall, we demonstrated that KDM6B serves as a positive circulation trigger during osteogenic differentiation by decreasing the ratio of M1/M2 both in vitro and in vivo. Collectively, these results provide insight into basic research in the field of osteoporosis and bone repair.

Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow.

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 grading process in facial palsy (FP) patients is crucial for time- and cost-effective therapy decision-making. The House-Brackmann scale (HBS) represents the most commonly used classification system in FP diagnostics. This study investigated the benefits of linking machine learning (ML) techniques with the HBS. Methods: Image datasets of 51 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2020 and May 2021, were used to build the neural network. A total of nine facial poses per patient were used to automatically determine the HBS. Results: The algorithm had an accuracy of 98%. The algorithm processed the real patient image series (i.e., nine images per patient) in 112 ms. For optimized accuracy, we found 30 training runs to be the most effective training length. Conclusion: We have developed an easy-to-use, time- and cost-efficient algorithm that provides highly accurate automated grading of FP patient images. In combination with our application, the algorithm may facilitate the FP surgeon’s clinical workflow.