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Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all topics that fall under the heading of artificial intelligence (AI) and have gained popularity in recent years. ML involves the application of algorithms to automate decision-making processes using models that have not been manually programmed but have been trained on data. ANNs that are a part of ML aim to simulate the structure and function of the human brain. DL, on the other hand, uses multiple layers of interconnected neurons. This enables the processing and analysis of large and complex databases. In medicine, these techniques are being introduced to improve the speed and efficiency of disease diagnosis and treatment. Each of the AI techniques presented in the paper is supported with an example of a possible medical application. Given the rapid development of technology, the use of AI in medicine shows promising results in the context of patient care. It is particularly important to keep a close eye on this issue and conduct further research in order to fully explore the potential of ML, ANNs, and DL, and bring further applications into clinical use in the future.

Regular physical activity has a beneficial impact on the cardiovascular system. However, the intense and prolonged exertion typical of professional athletes and amateur marathon runners can lead to adaptive changes in the heart. These changes encompass both structural and functional modifications, which may have positive or negative effects on cardiac function and contribute to the development of so-called “athlete’s heart.” Prolonged exercise induces adaptations at the molecular and cellular levels, including altered gene expression and remodeling of myocardial proteins. It may also cause transient elevations in biomarkers such as N-terminal pro-brain natriuretic peptide (NT-proBNP) and high-sensitivity troponin. Some athletes experience cardiac arrhythmias, including atrial fibrillation. Morphological changes, such as myocardial hypertrophy or chamber dilation, can be assessed using echocardiography. Studies have reported potentially benign valvular abnormalities, as well as cases of myocardial fibrosis and arrhythmias. Early diagnosis of cardiac conditions in marathon runners is essential for effective prevention and health monitoring. This article reviews the current data on cardiac changes in endurance athletes, based on the literature from the past decade.

As the number of smartphones increases, so does the number of medical apps. Medical mobile applications are widely used in many medical fields by both patients and doctors. However, there are still few approved mobile applications that can be used in the diagnostic-therapeutic process and radiological apps are affected as well. We conducted our research by classifying radiological applications from the Google Play® store into appropriate categories, according to our own qualification system developed by researchers for the purposes of this study. In addition, we also evaluated apps from the App Store®. The radiology application rating system we created has not been previously used in other articles. Out of 228 applications from the Google Play store, only 6 of them were classified as “A” category with the highest standard. Apps from the App Store (157) were not categorized due to the lack of download counts, which was necessary in our app-rating system. The vast majority of applications are for educational purposes and are not used in clinical practice. This is due to the need of obtaining special permits and certificates from relevant institutions in order to use them in medical practice. We recommend applications from the Google Play store that have been classified in the “A” category, evaluating them as the most valuable. App Store apps data is described and presented in the form of diagrams and tables.

In recent years, deep neural networks have enabled countless innovations in the field of image classification. Encouraged by success in this field, researchers worldwide have demonstrated how to use Convolutional Neural Network techniques in medical imaging problems. In this article, the results were obtained through the use of the EfficientNet in the task of classifying 14 different diseases based on chest X-ray images coming from the NIH (National Institutes of Health) ChestX-ray14 dataset. The approach addresses dataset imbalances by introducing a custom split to ensure fair representation. Binary cross entropy loss is utilized to handle the multi-label difficulty. The model architecture comprises an EfficientNet backbone for feature extraction, succeeded by sequential layers including GlobalAveragePooling, Dense, and BatchNormalization. The main contribution of this paper is a proposed solution that outperforms previous state-of-the-art deep learning models average area under the receiver operating characteristic curve—AUC-ROC (score: 84.28%). The usage of the transfer-learning technique and traditional deep learning engineering techniques was shown to enable us to obtain such results on consumer-class GPUs (graphics processing units).

This study evaluates the effectiveness of the ChatGPT-3.5 language model in providing correct answers to pathomorphology questions as required by the State Speciality Examination (PES). Artificial intelligence (AI) in medicine is generating increasing interest, but its potential needs thorough evaluation. A set of 119 exam questions by type and subtype were used, which were posed to the ChatGPT-3.5 model. Performance was analysed with regard to the success rate in different question categories and subtypes. ChatGPT-3.5 achieved a performance of 45.38%, which is significantly below the minimum PES pass threshold. The results achieved varied by question type and subtype, with better results in questions requiring \"comprehension and critical thinking\" than \"memory\". The analysis shows that, although ChatGPT-3.5 can be a useful teaching tool, its performance in providing correct answers to pathomorphology questions is significantly lower than that of human respondents. This conclusion highlights the need to further improve the AI model, taking into account the specificities of the medical field. Artificial intelligence can be helpful, but it cannot fully replace the experience and knowledge of specialists.

Introduction:In the 21st century’s era of rapid technological advancement, ChatGPT-3.5, an artificial intelligence (AI) language model, is scrutinized for its application in dermatology. Using 119 questions from the National Specialist Examination (PES), we assess ChatGPT-3.5’s performance by comparing it to human skills and addressing ethical implications.Objective:Our primary aim is to evaluate ChatGPT-3.5’s proficiency in responding to 119 dermatology questions from the PES. The study emphasizes ethical considerations and compares the model’s knowledge and skills to those of human dermatologists.Material and methods:Utilizing the 2023 PES question database, questions were categorized by Bloom’s taxonomy and thematic content. ChatGPT-3.5, version of 3 August 2023, answered 119 questions in five sessions, allowing for a probabilistic evaluation. Statistical analyses, conducted using R Studio, assessed correctness, confidence, and difficulty.Results:ChatGPT-3.5 achieved a 49.58% correct response rate, below the 60% passing threshold. No significant differences in difficulty or correlations between difficulty and certainty were observed. Varied performance across question types highlighted strengths and weaknesses. Despite suboptimal results, ChatGPT-3.5’s differential performance offers insights, suggesting future improvements. The study advocates for ongoing research into AI integration in dermatology, envisioning a promising role for AI in assisting dermatologists.Conclusions:Ethical considerations are crucial for effective AI introduction, minimizing errors, and enhancing dermatological healthcare quality, fostering optimism for AI’s evolving role in dermatology.

Cerebral vascular angiography, or digital subtraction angiography (DSA), is essential for diagnosing neurological conditions but poses radiation risks. This study aims to analyze the impact of examination parameters and patient characteristics on the radiation dose received during DSA to optimize safety and minimize exposure. A retrospective analysis of 251 DSA procedures using the GE Innova IGS 630 dual-plane instrument was conducted. Data on dose area product (DAP) and air kerma (KERMA), along with patient and examination details, were collected. Statistical analyses, including Mann–Whitney, Kruskal–Wallis, and Spearman rank correlation tests, assessed the relationships between variables and radiation dose outcomes. Significant correlations were found between the sides examined (left, right, or both) and DAP (p < 0.0001) and KERMA (p < 0.0001) values, with bilateral studies showing the highest values. The post hoc Dunn tests showed that the ‘L + P’ group significantly differs from both the right group (p < 0.0001 and the left group (p < 0.0001). There is no significant difference between the ‘P’ group and the ‘L’ group (p-value = 0.53). These results suggest that the right and left (both) group have unique KERMA mGy values compared to the other two groups. A strong correlation (rS = 0.87) existed between DAP and KERMA. The number of projections significantly impacted radiation dose (rS = 0.61). Tube parameters (kV and mA) and skull size had low correlations with DAP and KERMA. Optimizing imaging protocols and individualizing parameters can significantly enhance patient safety and diagnostic efficacy while also reducing occupational exposure for medical staff.

(1) Background. Digital subtraction angiography (DSA) is indispensable for diagnosing cerebral aneurysms due to its superior imaging precision. However, optimizing X-ray parameters is crucial for accurate diagnosis, with X-ray tube settings significantly influencing image quality. Understanding the relationship between skull dimensions and X-ray parameters is pivotal for tailoring imaging protocols to individual patients. (2) Methods. A retrospective analysis of DSA data from a single center was conducted, involving 251 patients. Cephalometric measurements and statistical analyses were performed to assess correlations between skull dimensions and X-ray tube parameters (voltage and current). (3) Results. The study revealed significant correlations between skull dimensions and X-ray tube parameters, highlighting the importance of considering individual anatomical variations. Gender-based differences in X-ray parameters were observed, emphasizing the need for personalized imaging protocols. (4) Conclusions. Personalized approaches to DSA imaging, integrating individual anatomical variations and gender-specific differences, are essential for optimizing diagnostic outcomes. While this study provides valuable insights, further research across multiple centers and diverse imaging equipment is warranted to validate these findings.

Introduction: The rapid development of artificial intelligence (AI) has sparked a desire to analyse its potential applications in medicine. The aim of this article is to present the effectiveness of the ChatGPT advanced language model in the context of the pass rate of the polish National Specialty Examination (PES) in nuclear medicine. It also aims to identify its strengths and limitations through an in-depth analysis of the issues raised in the exam questions. Methods: The PES exam provided by the Centre for Medical Examinations in Łódź, consisting of 120 questions, was used for the study. The questions were asked using the openai.com platform, through which free access to the GPT-3.5 model is available. All questions were classified according to Bloom's taxonomy to determine their complexity and difficulty, and according to two authors' subcategories. To assess the model's confidence in the validity of the answers, each questions was asked five times in independent sessions. Results: ChatGPT achieved 56%, which means it did not pass the exam. The pass rate is 60%. Of the 117 questions asked, 66 were answered correctly. In the percentage of each type and subtype of questions answered correctly, there were no statistically significant differences. Conclusion: Further testing is needed using the questions provided by Centre for Medical Examinations from the nuclear medicine specialty exam to evaluate the utility of the ChatGPT model. This opens the door for further research on upcoming improved versions of the ChatGPT.

Introduction and objective: The COVID-19 pandemic has had a significant impact on peoples’ mental health. The literature is abundant with studies describing levels of aggression, anxiety, and alcohol consumption during 2020–2021. However, it is noteworthy that little attention has been paid to the responses of men to the pandemic. The aim of this study was to assess adaptive abilities in response to the COVID-19 pandemic among a group of Polish men. Particular focus was placed on the role of ego-resiliency. Materials and methods: The study utilised an online survey conducted in two periods: 24 April to 8 May 2020 and 5 February to 6 March 2022. Participants included 125 men aged 18–66 in the first period and 136 men aged 18–57 in the second period. The survey included the Generalized Anxiety Disorder-7 Scale, Alcohol Use Disorders Identification Test, Buss–Perry Aggression Questionnaire, and Ego-Resiliency Scale. Results: In the second phase of the study, anxiety showed a stronger negative correlation with ego-resiliency. Ego-resiliency was also correlated with hostility and generalised aggression. However, correlations between verbal aggression and resilience were weaker in the second period. Resilience showed comparable correlations with alcohol consumption, physical aggression, and anger in both periods. Conclusions: Ego-resiliency emerged as a protective factor against anxiety, anger, and hostility during the COVID-19 pandemic among the studied groups of men. It appears to be a stable and integral part of personality, unaffected by the pandemic period. The role of mental resilience in relation to verbal aggression remains unclear.