Despite the growing popularity of mobile devices, they still have not found widespread use in medicine. This is due to the procedures in a given place, differences in the availability of mobile devices between individual institutions or lack of appropriate legal regulations and accreditation by relevant institutions. Numerous studies have been conducted and compared the usability of mobile solutions designed for diagnostic images evaluation on various mobile devices and applications with classic stationary descriptive stations. This study is an attempt to compare the usefulness of currently available mobile applications which are used in the medical industry, focusing on imaging diagnostics. As a consequence of the healthcare sector’s diversity, it is also not possible to design a universal mobile application, which results in a multitude of software available on the market and makes it difficult to reliably compile and compare studies included in this systematic review. Despite these differences, it was possible to identify both positive and negative features of portable methods analyzing radiological images. The mobile application of the golden mean in hospital infrastructure should be widely available, with convenient and simple usage. Our future research will focus on development in the use of mobile devices and applications in the medical sector.

Background: The rapid development of artificial intelligence (AI) technologies like OpenAI's Generative Pretrained Transformer (GPT), particularly ChatGPT, has shown promising applications in various fields, including medicine. This study evaluates ChatGPT's performance on the Polish Final Medical Examination (LEK), comparing its efficacy to that of human test-takers. Methods: The study analyzed ChatGPT's ability to answer 196 multiple-choice questions from the spring 2021 LEK. Questions were categorized into "clinical cases" and "other" general medical knowledge, and then divided according to medical fields. Two versions of ChatGPT (3.5 and 4.0) were tested. Statistical analyses, including Pearson's χ2 test, and Mann-Whitney U test, were conducted to compare the AI's performance and confidence levels. Results: ChatGPT 3.5 correctly answered 50.51% of the questions, while ChatGPT 4.0 answered 77.55% correctly, surpassing the 56% passing threshold. Version 3.5 showed significantly higher confidence in correct answers, whereas version 4.0 maintained consistent confidence regardless of answer accuracy. No significant differences in performance were observed across different medical fields. Conclusions: ChatGPT 4.0 demonstrated the ability to pass the LEK, indicating substantial potential for AI in medical education and assessment. Future improvements in AI models, such as the anticipated ChatGPT 5.0, may enhance further performance, potentially equaling or surpassing human test-takers.

Background Artificial intelligence (AI), particularly language models such as ChatGPT, is gaining importance in medical education and knowledge assessment. Previous studies have demonstrated the growing effectiveness of AI in solving medical exams, including the Final Medical Examination (LEK) and Polish State Specialization Exam (PES) in various specialties, raising questions about its usefulness as a tool to support specialist training processes. Objective The aim of this study was to assess the effectiveness of the latest ChatGPT-4o model in solving the PES in ophthalmology. The analysis focused on the accuracy of the answers and the model's declared confidence level to evaluate its potential educational usefulness. Methods The study was based on the official PES ophthalmology exam (Spring 2024), consisting of 120 multiple-choice questions. The ChatGPT-4o model was familiarized with the exam regulations and questions, which were input in Polish. The effectiveness of the answers was assessed based on the Medical Education Center (CEM) answer key, as well as the model's declared confidence level (on a scale of 1 to 5). The questions were divided into clinical and theoretical categories. Data were analyzed statistically using the chi-square test and the Mann-Whitney U test. Results The model provided 94 correct answers (78.3%), exceeding the passing threshold. No significant difference in effectiveness was observed between clinical and non-clinical questions (p = 0.709). The analysis of the confidence level revealed that correct answers were significantly more often provided with higher confidence (p < 0.001), suggesting that the model’s self-assessment could be an indicator of answer accuracy. Conclusions ChatGPT-4o demonstrated high effectiveness in the PES ophthalmology exam, confirming the potential of AI in specialist education. The confidence level of answers could serve as a useful tool in assessing the reliability of responses. Despite promising results, expert supervision and further research in various medical fields are necessary before wider implementation of AI models in medical education.

Introduction: Skin diseases account for about 7% of all occupational diseases in Europe. Allergic contact dermatitis (ACD) is one of the most common occupational skin diseases. Therefore, it constitutes the major health and economic problem. Increasing the detectability of ACD would...

Introduction: There are 2 methods for correcting facial deformities resulting from facial nerve paralysis – dynamic and static. Although dynamic methods are the standard approach in selected patient groups, static facial suspension using synthetic material provides a minimally invasive alternative for those who do not qualify for them. Aim: Presenting experiences with the use of non-absorbable polypropylene mesh in the Department of Plastic Surgery in Polanica-Zdrój and reviewing the literature related to static facial reanimation methods. Materials and methods: Fourteen patients underwent static facial tissue suspension using non-absorbable polypropylene mesh and 12 patients underwent surgery using classical autologous tissue techniques between 2019 and 2023. Indications for surgical treatment were analyzed, and preliminary results of treatment using both methods were compared. Results: Compared to autologous tissue methods, surgeries using synthetic mesh were associated with shorter hospitalization times. No long-term postoperative complications were observed in any patient. There were no differences in treatment outcomes between autologous tissue and synthetic mesh techniques, and all patients assessed the outcome as favorable. Discussion: Treatment of facial nerve paralysis sequelae using static techniques should be considered for patients not qualifying for surgery with dynamic methods and/or not consenting to such procedures. Using synthetic material as an alternative to autologous material reduces surgical time and avoids donor-site injuries and prolonged recovery. Conclusions: Facial suspension with synthetic material is an effective, low-risk procedure for static correction of facial nerve paralysis sequelae. Study significance: Emphasizing the importance of static methods in facial reanimation.