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.

SARS-CoV-2 virus can not only damage the respiratory system but may also pose a threat to other organs, such as the heart or vessels. This review focuses on cardiovascular complications of COVID-19, including acute cardiac injury, arrhythmias, biomarkers, accompanying comorbidities and outcomes in patients diagnosed with SARS-CoV-2 infection. The research was conducted on the databases: PubMed, Springer, ScienceDirect, UpToDate, Oxford Academic, Wiley Online Library, ClinicalKey. Fifty-six publications from 1 November 2020 till 15 August 2021 were included in this study. The results show that cardiac injury is present in about 1 in 4 patients with COVID-19 disease, and it is an independent risk factor, which multiplies the death rate several times in comparison to infected patients without myocardial injury. New-onset cardiac injury occurs in nearly every 10th patient of the COVID-19-suffering population. Comorbidities (such as hypertension, cardiovascular disease and diabetes) severely deteriorate the outcome. Therefore, patients with SARS-CoV-2 infection should be carefully assessed in terms of cardiac medical history and possible cardiological complications.

Drug-eluting stents (DES) demonstrated superior efficacy when compared to bare metal stents and plain-old balloon angioplasty and are nowadays used in almost all percutaneous revascularization procedures. The design of the stent platforms is constantly improving to maximize its efficacy and safety. Constant development of DES includes adoption of new materials used for scaffold production, new design types, improved overexpansion abilities, new polymers coating and, finally, improved antiproliferative agents. Especially nowadays, with the immense number of available DES platforms, it is crucial to understand how different aspects of stents impact the effect of their implantation, as subtle differences between various stent platforms could impact the most important issue—clinical outcomes. This review discusses the current status of coronary stents and the impact of stent material, strut design and coating techniques on cardiovascular outcomes.

This systematic review focuses on using artificial intelligence (AI) to detect COVID-19 infection with the help of X-ray images. Methodology: In January 2022, the authors searched PubMed, Embase and Scopus using specific medical subject headings terms and filters. All articles were independently reviewed by two reviewers. All conflicts resulting from a misunderstanding were resolved by a third independent researcher. After assessing abstracts and article usefulness, eliminating repetitions and applying inclusion and exclusion criteria, six studies were found to be qualified for this study. Results:The findings from individual studies differed due to the various approaches of the authors. Sensitivity was 72.59%-100%, specificity was 79%-99.9%, precision was 74.74%-98.7%, accuracy was 76.18%-99.81%, and the area under the curve was 95.24%-97.7%. Conclusion: AI computational models used to assess chest X-rays in the process of diagnosing COVID-19 should achieve sufficiently high sensitivity and specificity. Their results and performance should be repeatable to make them dependable for clinicians. Moreover, these additional diagnostic tools should be more affordable and faster than the currently available procedures. The performance and calculations of AI-based systems should take clinical data into account.

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