The activities related to medical device market can be divided into pre-market and post-market surveillance. Pre-market processes have been defined by Medical Device Directives since 1992. These directives define all aspects of medical device design, production, testing, approval and certification. Appropriate standards have been adopted to support these activities. The CE mark issued by European Notified Bodies confirms that the medical device complies with safety and performance standards related to its class and that is therefore safe for intended usage. Post-market surveillance is not as well defined, so the new Medical Device Regulation addresses the identified gap and emphasizes the importance of standardizing and harmonizing the system for surveillance of medical devices already in use. The MDRs require stakeholders to monitor the quality, performance and safety of a device throughout its life cycle and to take corrective or preventive action when necessary. In this paper, we discuss the possibility of using artificial intelligence on Big Data structures resulting from the comprehensive methodology of post-market surveillance.

Artificial Intelligence (AI) has been drawing attention in the field of medical devices. However, due to system complexity, the variability of their architecture, as well as ethical and regulatory concerns there is an ongoing need to analyze its application and performance.This study presents a narrative commentary on the applications of artificial neural networks (ANN) and machine learning (ML) algorithms in medical devices, past, current and future perspectives of application. One research focus of this study was on identifying problems and issues related to the implementation of AI in medical devices. The commentary is based on scientific articles published in PubMed, Scopus ad ScienceDirect databases, official publications of international organizations: European Comission (EC), Food and Drug Administration (FDA), and World Health Organisation (WHO) published in 2009 - 2020 period. AI is revolutionizing healthcare, from medical applications to clinical engineering. However, before grasp-ing the full potential ethical, legal and social concerns need to be resolved and its application needs to be harmonized and regulated regarding equitable access, privacy, appropriate uses and users, liability and bias and inclusiveness.

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.

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.

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.

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.