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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.

Visual discrimination of tissue during surgery can be challenging since different tissues appear similar to the human eye. Hyperspectral imaging (HSI) removes this limitation by associating each pixel with high-dimensional spectral information. While previous work has shown its general potential to discriminate tissue, clinical translation has been limited due to the method’s current lack of robustness and generalizability. Specifically, the scientific community is lacking a comprehensive spectral tissue atlas, and it is unknown whether variability in spectral reflectance is primarily explained by tissue type rather than the recorded individual or specific acquisition conditions. The contribution of this work is threefold: (1) Based on an annotated medical HSI data set (9059 images from 46 pigs), we present a tissue atlas featuring spectral fingerprints of 20 different porcine organs and tissue types. (2) Using the principle of mixed model analysis, we show that the greatest source of variability related to HSI images is the organ under observation. (3) We show that HSI-based fully-automatic tissue differentiation of 20 organ classes with deep neural networks is possible with high accuracy (> 95%). We conclude from our study that automatic tissue discrimination based on HSI data is feasible and could thus aid in intraoperative decisionmaking and pave the way for context-aware computer-assisted surgery systems and autonomous robotics.

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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.

Diabetic wounds are characterized by drug‐resistant bacterial infections, biofilm formation, impaired angiogenesis and perfusion, and oxidative damage to the microenvironment. Given their complex nature, diabetic wounds remain a major challenge in clinical practice. Reactive oxygen species (ROS), which have been shown to trigger hyperinflammation and excessive cellular apoptosis, play a pivotal role in the pathogenesis of diabetic wounds. ROS‐scavenging nanosystems have recently emerged as smart and multifunctional nanomedicines with broad synergistic applicability. The documented anti‐inflammatory and pro‐angiogenic ability of ROS‐scavenging treatments predestines these nanosystems as promising options for the treatment of diabetic wounds. Yet, in this context, the therapeutic applicability and efficacy of ROS‐scavenging nanosystems remain to be elucidated. Herein, the role of ROS in diabetic wounds is deciphered, and the properties and strengths of nanosystems with ROS‐scavenging capacity for the treatment of diabetic wounds are summarized. In addition, the current challenges of such nanosystems and their potential future directions are discussed through a clinical‐translational lens.