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Modern research data indicate that an analysis of human exhaled air composition allows to quickly and non-invasively diagnose a wide range of diseases, from asthma and chronic obstructive pulmonary disease to lung cancer, tuberculosis and coronavirus infection COVID-19 [1]. In this work we discuss the advantages and disadvantages of the main modern methods and approaches leading to the exhaled air composition determination as well as various diseases diagnostics based on the data obtained. It will be shown that the analysis by a portable “electronic nose” based on an array of semi-selective gas sensors, having different operating principles and complemented by machine learning methods and artificial intelligence technologies, allows for differential diagnosis of the above diseases (using the example of COVID-19, tuberculosis and asthma) in “black box” mode without identifying of specific markers related to a particular disease. Elaborated approach significantly speeds up and reduces the cost of the diagnostic process. A portable gas analyzer device for non-invasive rapid diagnostics of COVID-19, capable of determining COVID-19 infection with high sensitivity and specificity in a short time (1-2 minutes), incl. in asymptomatic cases will be demonstrated.
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