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.

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.