(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.

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.

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.

Background: Facial overfilled syndrome is an adverse event following minimally invasive soft tissue filler injections. It presents in most cases as excess midfacial volume and/or as unnatural smile which is difficult to detect due to the absence of standardized evaluation methods.Objective: To showcase how to identify, evaluate, and treat facial overfilled syndrome by utilizing facial ultrasound and simultaneous hyaluronidase injections.Methods: Twenty‐eight consecutive patients (26 females, 2 males) were enrolled in this study in which facial ultrasound was performed to evaluate the location previously implanted filler material. The position of the oral commissure was objectively measured in relation to bony landmarks, and the severity of lateral canthal lines was assessed by independent and blinded raters.Results: The material was identified in 35.7% inside the subdermal fatty layer, in 28.6% inside the deep supra‐periosteal fatty layer, in 10.7% inside the fibrous layer deep to the subdermal fatty layer, whereas in 25.0%, the product was not possible to locate clearly inside one specific layer. On average, 81.6 I.U. [range: 75–150] of hyaluronidase were injected. Lateral canthal line severity was before the treatment 2.28 (1.4) and was after the hyaluronidase treatment 2.02 (1.3) with p = 0.578. The position of the oral commissure increased by 0.60 cm in vertical and by 0.30 cm in horizontal directions (both p < 0.001).Conclusion: Facial overfilled syndrome following aesthetic soft tissue filler injections can present as excess midfacial volume but also as unnatural smile. Targeted hyaluronidase injections into the culprit pockets inside the midfacial soft tissues have shown to re‐establish a natural smile, to reduce excess midfacial volume, and to decrease lateral canthal line severity.

Adverse vascular event management following hyaluronic acid-based aesthetic injections relies on the administration of hyaluronidase which is capable to enzymatically degrade the injected product and to improve clinical symptoms. Two protocols are currently available to manage such complications: “ultrasound guided targeted” and “flooding”. To compare the 2 protocols by means of amount of hyaluronidase volume utilized, and onset and degree of clinical improvement. A comparative case series of 39 patients was retrospectively evaluated which were initially treated with the “flooding” protocol and then treated with the “ultrasound guided targeted” due to no or little improvement. The “ultrasound-guided targeted” protocol utilized a total of 122.5 iu (34) whereas the “flooding” protocol utilized 1519.4 iu (1137) which represents a statistically significant reduced amount of injected hyaluronidase with p = 0.028. There was no clinical improvement in 92.3% and only little improvement in 7.7% of the treated patients following the first applied “flooding” protocol but there was a 100% immediate improvement when treated with the “ultrasound-guided targeted” protocol. Ultrasound imaging revealed that the application of hyaluronidase normal blood flow was restored both in the perivascular space and in the superficially located subdermal soft tissues. Despite its limitations in study design, this retrospectively evaluated case series revealed that the ultrasound guided targeted protocol utilized less hyaluronidase material and restored faster clinically visible symptoms. The effect of this protocol is best explained by the perforasome concept which will need to be investigated further in future studies.

Background: An increasing number of soft tissue filler procedures in the nasal region has been reported. Concomitant with demand, the number of complications has risen due to the difficulty in administering filler in a region where soft tissue layering is complex.Objectives: The authors sought to describe the layered soft tissue arrangement of the nose as it relates to the underlying arterial vasculature and to define safer zones for nasal filler enhancement.Methods: A total of 60 (28 males and 32 females) study participants were investigated with respect to their layered anatomy in the midline of the nose utilizing ultrasound imaging. The presence and extent of the layered arrangement was examined as well as the depth of the arterial vasculature.Results: In the mid-nasal dorsum, a 5-layer arrangement was observed in 100% (n = 60) of all investigated cases, whereas it was found to be absent in the nasal radix and tip. The 5-layer arrangement showed an average extent of 26.7% to 67.5% in relation to nasal length. The nasal arteries coursed superficially in 91.7% of all cases in the nasal radix, in 80% in the mid-nasal dorsum, and in 98.3% in the nasal tip.Conclusions: Soft tissue filler administration in the nose carries the highest risk for irreversible vision loss compared with any other facial region. The safety of soft tissue filler rhinoplasty procedures is enhanced by knowledge of the layered anatomy of the nose, the location and depth of the major nasal vasculature, and employment of maneuvers to decrease the risk of blindness.