The world is facing an unprecedented outbreak affecting all aspects of human lives which is caused by the COVID-19 pandemic. Due to the virus novelty, healthcare systems are challenged by a high rate of patients and the shortage of medical products. To address an increased need for essential medical products, national authorities, worldwide, made various legislative concessions. This has led to essential medical products being produced by automotive, textile and other companies from various industries and approved under the emergency use authorizations or legal concessions of national regulatory bodies. This paper presents a narrative commentary of the available documentation on emergency use authorizations and legal concessions for medical products during COVID-19 pandemic. The basis for narrative commentary includes scientific articles published in Web of Science, Scopus, PubMed and Embase databases, official publications of international organizations: Food and Drug Agency (FDA), World Health Organisation (WHO), World Bank and United Nations (UN), and national regulatory agency reports in native languages (English, German, Bosnian, and Croatian) published from November 1, 2019 to May 1, 2020. This paper focuses on three types of essential medical products: mechanical ventilators, personal protective equipment (PPE) and diagnostic tests. Evidence-informed commentary of available data and potential identified risks of emergency use authorizations and legal concessions is presented. It is recognized that now more than ever, raising global awareness and knowledge about the importance of respecting the essential requirements is needed to guarantee the appropriate quality, performance and safety of medical products, especially during outbreak situation, such as the COVID-19 pandemic. Emergency use authorizations for production, import and approval of medical products should be strictly specified and clearly targeted from case to case and should not be general or universal for all medical products, because all of them are associated with different risk level. Presented considerations and experiences should be taken as a guide for all possible future outbreak situations to prevent improvised reactions of national regulatory bodies.

Increasing incidence of cardiovascular disease and their mortality rate render them as second leading cause of death worldwide. Artificial Intelligence (AI) is used in many fields of science and industry, but also has found its use in medicine for diagnosis, treatment and prediction of diseases. This paper presents the review of AI application in cardiology. The review is based on research papers published in Medline database. Findings of the review indicate that, according to accuracy parameter, the overall performance of AI based models for cardiovascular application is above 83%. Based on the results, AI algorithms and deep learning can be rendered as accurate, hence showing possibility to be used as a diagnostic tool now and in the future. New era of modern diagnosing is coming and Artificial Intelligence has the potential to change the way in which medicine is practiced.

This paper presents the results of development of Artificial Neural Networks (ANNs) for prediction of medical device performance based on conformity assessment data. Conformity assessment data of medical devices was obtained from periodical inspections conducted by ISO 17020 accredited laboratory during 2015–2019 period. For the development of ANNs, 1738 samples of conformity assessment of infusion and perfusor pumps was used. Out of the overall number of samples, 1391 (80%) of them were used during system development and 346 (20%) samples were used for subsequent validation of system performance. During system development, the impact on overall system accuracy of different number of neurons in hidden layer and the activation functions was tested. Also, two neural network architectures were tested: feedforward and feedback. The results show that feedforward neural network architecture with 10 neurons in single hidden layer has the best performance. The overall accuracy of that neural network is 98.06% for performance prediction of perfusor pumps and 98.83% for performance prediction of infusion pumps. The recurrent neural network resulted in accuracy of 98.41% for both infusion pumps and perfusor pumps. The results show that conformity assessment data obtained through yearly inspections of medical devices can successfully be used for prediction of performance of single medical device. This is very important in increasing the safety and accuracy of diagnosis and treatment of patients.

Microbiology laboratory is a type of medical laboratory and should be safe and efficient environment. Even it is not a mandatory for the accreditation in most of the countries, ISO/IEC 15189 remains the most common reference for quality of work in medical laboratories. It is mostly based on good laboratory practices and is oriented to support accurate clinical decisions. ISO/IEC 15189 has potential to become very effective instrument for development and improvement of medical laboratories. Results from laboratory should guide the majority of clinical decisions and help in providing adequate patient care. This article provides a simple approach to meet the minimum requirements set. To achieve intended goal and strictly follow the requirements proposed in the standard, the trained and well-motivated laboratory staff is necessary to implement the system. The objective of this article is for it to be used as a guideline for evaluation and implementation of the ISO 15189.

Glucose is a main source of energy in human body and its regulation is controlled by a biological mechanism with organ/cell interactions that are related to glucose-insulin dynamics. This paper presents the model of physiological behaviors of glucose-insulin regulatory mechanism. This model allows investigation of blood glucose dynamics dependency on food intake. The model presented in this paper discusses several parameters within this complex system.