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.

Diagnostic imaging has become an integral part of the healthcare system. In recent years, scientists around the world have been working on artificial intelligence-based tools that help in achieving better and faster diagnoses. Their accuracy is crucial for successful treatment, especially for imaging diagnostics. This study used a deep convolutional neural network to detect four categories of objects on digital chest X-ray images. The data were obtained from the publicly available National Institutes of Health (NIH) Chest X-ray (CXR) Dataset. In total, 112,120 CXRs from 30,805 patients were manually checked for foreign objects: vascular port, shoulder endoprosthesis, necklace, and implantable cardioverter-defibrillator (ICD). Then, they were annotated with the use of a computer program, and the necessary image preprocessing was performed, such as resizing, normalization, and cropping. The object detection model was trained using the You Only Look Once v8 architecture and the Ultralytics framework. The results showed not only that the obtained average precision of foreign object detection on the CXR was 0.815 but also that the model can be useful in detecting foreign objects on the CXR images. Models of this type may be used as a tool for specialists, in particular, with the growing popularity of radiology comes an increasing workload. We are optimistic that it could accelerate and facilitate the work to provide a faster diagnosis.

Objectives: The purpose of this study is to evaluate the performance of our deep learning algorithm in calculating cardiothoracic ratio (CTR) and thus in the assessment of cardiomegaly or pericardial effusion occurrences on chest radiography (CXR). Methods: From a database of 8000 CXRs, 13 folders with a comparable number of images were created. Then, 1020 images were chosen randomly, in proportion to the number of images in each folder. Afterward, CTR was calculated using RadiAnt Digital Imaging and Communications in Medicine (DICOM) Viewer software (2023.1). Next, heart and lung anatomical areas were marked in 3D Slicer. From these data, we trained an AI model which segmented heart and lung anatomy and determined the CTR value. Results: Our model achieved an Intersection over Union metric of 88.28% for the augmented training subset and 83.06% for the validation subset. F1-score for subsets were accordingly 90.22% and 90.67%. In the comparative analysis of artificial intelligence (AI) vs. humans, significantly lower transverse thoracic diameter (TTD) (p < 0.001), transverse cardiac diameter (TCD) (p < 0.001), and CTR (p < 0.001) values obtained using the neural network were observed. Conclusions: Results confirm that there is a significant correlation between the measurements made by human observers and the neural network. After validation in clinical conditions, our method may be used as a screening test or advisory tool when a specialist is not available, especially on Intensive Care Units (ICUs) or Emergency Departments (ERs) where time plays a key role.

Background: The COVID-19 pandemic, which affected the entire global population, had an impact on our health and quality of life. Many people had complications, were hospitalised or even died due to SARS-CoV-2 infection. The health systems of many countries had to radically change their way of functioning and scientists around the world worked intensively to develop a vaccine for the SARS-CoV-2 virus. Aim: The aim of this work is to assess the quality of life of patients who were hospitalised for COVID-19, using the SF-36 questionnaire. Methods: Between May and August 2022, we conducted a telephone assessment of quality of life in patients who were hospitalised for COVID-19 at the Temporary Hospital in Pyrzowice (Silesia, Poland), between November 2021 and January 2022. Results: Quality of life was significantly lower in women (p = 0.040), those with DM2 (p = 0.013), CKD (p = 0.041) and the vaccinated (p = 0.015). Conclusions: People with chronic kidney disease, diabetes mellitus and women had a lower quality of life after COVID-19 disease. However, people who were vaccinated for SARS-CoV-2 had a lower quality of life than non-vaccinated people did. This is possibly due to the higher mean age, and probably the higher disease burden, in the vaccinated group.

Hypercholesterolemia is the main cardiovascular (CV) risk factor with a large body of evidence. Our aim was to assess the achievement of the main therapeutic goal of Low-Density Lipoprotein Cholesterol (LDL-C) in patients with a very high CV risk and a high-dose statin therapy. The study group consisted of 1413 consecutive patients hospitalised at the Upper-Silesian Medical Centre in Katowice due to acute myocardial infarction (AMI) treated with atorvastatin ≥ 40 mg or rosuvastatin ≥ 20 mg. The lipid profile was performed on admission and within 12 months after AMI. The main therapeutic goal was defined as LDL-C < 55 mg%. The study group (n = 1413) included 979 males (69.3%) with arterial hypertension (83.3%), diabetes (33.5%), peripheral artery disease (13.6%) and nicotinism (46.2%). In the study group, only 61 patients (4.3%) were additionally taking ezetimibe. During hospitalisation, the primary LDL-C goal was found in only 186 patients (13.2%). Subsequently, a follow-up lipidogram within 12 months was performed in 652 patients (46%), and the therapeutic goal was achieved in 255 patients (39%). There were 258 (18.26%) patients who died within 12 months after myocardial infarction. The lowest mortality rate was found in the subgroup of patients with LDL-C < 55 mg% during follow-up (11.02%). The primary lipid goal attainment among patients with a high-dose statin and a very high CV risk is low and far from the expected rate. Patients hospitalised for AMI should be given a combination of statin and ezetimibe more frequently. Low LDL-C levels measured at follow-up predict a lower risk of death at 12-month follow-up in a large group of patients.

The history of ketamine began in the 1960s. Ketamine is a specific anesthetic drug that exhibits analgesia, has a stabilizing impact on the cardiovascular system and does not significantly depress the respiratory system. It is responsible for causing the so-called dissociative effect. The effects of ketamine depend on the dose. Its mechanism of action is based primarily on the blockade of N-methyl D-aspartic acid receptors (NMDA) in the central nervous system. Due to its favorable action profile and small number of contraindications, ketamine is becoming an increasingly popular drug. In medicine, it is commonly used as an analgesic and an anesthetic. Studies conducted over recent years have noted that it can be effective in conditions such as: status epilepticus, status asthmaticus, agitation, alcohol dependence and alcohol withdrawal syndrome, depression, and suicidal thoughts. This paper reviews current reports on the uses of ketamine, beneficial especially to physicians with the Specialty in emergency medicine.