Despite the fact that technology is improving day by day and that the medical devices (MDs) are being constantly upgraded, their malfunction is not a rare occurrence. The aim of this research is to develop an expert system that can predict whether the device will satisfy functional and safety requirements during a regular inspection. This expert system can be seen as part of Industry 4.0 that is revolutionizing medical device management. In order to develop the system, five machine learning algorithms that are representative of each classifier group, were used: (1) Random Forest, (2) Decision Tree, (3) Support Vector Machine, (4) Naive Bayes, (5) k-Nearest Neighbour. The Decision Tree outperformed other classifiers achieving the classification accuracy of 100% with and without attribute selection applied on the dataset. This study showed that machine learning algorithms can be used in order to predict MDs performance and potential failures in order to make the process of maintenance of medical devices more convenient and sophisticated and it is one step in modernizing medical device management systems by utilizing artificial intelligence.

Abstract Poorly regulated and insufficiently supervised medical devices (MDs) carry high risk of performance accuracy and safety deviations effecting the clinical accuracy and efficiency of patient diagnosis and treatments. Even with the increase of technological sophistication of devices, incidents involving defibrillator malfunction are unfortunately not rare. To address this, we have developed an automated system based on machine learning algorithms that can predict performance of defibrillators and possible performance failures of the device which can affect performance. To develop an automated system, with high accuracy, overall dataset containing safety and performance measurements data was acquired from periodical safety and performance inspections of 1221 defibrillator. These inspections were carried out in period 2015–2017 in private and public healthcare institutions in Bosnia and Herzegovina by ISO 17,020 accredited laboratory. Out of overall number of samples, 974 of them were used during system development and 247 samples were used for subsequent validation of system performance. During system development, 5 different machine learning algorithms were used, and resulting systems were compared by obtained performance. The results of this study demonstrate that clinical engineering and health technology management benefit from application of machine learning in terms of cost optimization and medical device management. Automated systems, based on machine learning algorithms, can predict defibrillator performance with high accuracy. Systems based on Random Forest classifier with Genetic Algorithm feature selection yielded highest accuracy among other machine learning systems. Adoption of such systems will help in overcoming challenges of adapting maintenance and medical device supervision mechanism protocols to rapid technological development of these devices. Due to increased complexity of healthcare institution environment and increased technological complexity of medical devices, performing maintenance strategies in traditional manner is causing a lot of difficulties.

Respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD), are affecting a huge percentage of the world’s population with mortality rates exceeding those of lung cancer and breast cancer combined. The major challenge is the number of patients who are incorrectly diagnosed. To address this, we developed an expert diagnostic system that can differentiate among patients with asthma, COPD or a normal lung function based on measurements of lung function and information about patient’s symptoms. To develop accurate classification algorithms, data from 3657 patients were used and then independently verified using data from 1650 patients collected over a period of two years. Our results demonstrate that the expert diagnostic system can correctly identify patients with asthma and COPD with sensitivity of 96.45% and specificity of 98.71%. Additionally, 98.71% of the patients with a normal lung function were correctly classified, which contributed to a 49.23% decrease in demand for conducting additional tests, therefore decreasing financial cost.

Nanotechnology has shown its great potential in different fields of science such as medicine and pharmacy. This paper presents a review on artificial neural networks used in nanotechnology based on information gathered from different research. It is important to understand applications of artificial neural networks so that they can be used even more efficiently in future applications. Research papers summarized and compared here show different results in two fields of science. Artificial neural networks were made and proven to be useful in diagnostics and tracing diseases. The pharmaceutical industry has also shown to be a good candidate for the development of ANNs on the nanotechnology level. Regression analysis was used as a statistical method for presenting the best results from both fields observed. Root mean square error and mean error were calculated to measure the differences between values predicted by a model and the values actually observed from the environment that was being modelled. Based on individual results, each of the ANNs made were accurate enough to be considered as a diagnostic tool in fields of medicine and pharmacy. Performance is greater than 90% 10 out of 12 times, which is viewed in this paper. The multilayer perceptron ANN is mostly used. Based on the latest results, in upcoming years, one can expect better understanding and more research in the field of ANN applications in nanotechnology.