The aim: To optimize diagnostic of pathological processes in lungs affected by COVID-19, dynamic monitoring and clinical decision making using lung ultrasound in limited resources settings. Materials and methods: Between the onset of pandemics and January 2021, approximately 9000 patients have been treated for confirmed COVID-19 in the Olexandrivska Clinical Hospital. Assessment of all hospitalized patients included hematology, chemistries and proinflammatory cytokines – IL-6, CRP, procalcitonin, ferritin. Diagnosis was confirmed by PCR for SARS-CoV-2 RNA. Chest X-ray was performed in all hospitalized cases, while CT was available approximately in 30% of cases during hospital stay. Lung ultrasound was proactively utilized to assess the type and extent of lung damage and to monitor the progress of disease in patients hospitalized into the ICU and Infection Unit (n=135). Ultrasound findings were recorded numerically based on scales. Results: In the setting of СOVID-19, bedside lung ultrasound has been promptly recognized as a tool to diagnose and monitor the nature and extent of lung injury. Lung ultrasound is a real time assessment, which helps determine the nature of a pathologic process affecting lungs. In this paper the accuracy of bedside LUS, chest X-ray and computer tomography are compared based on clinical cases, typical for COVID-19 lung ultrasound appearance is evaluated. Described in article data is collected in one of the biggest facility that deals with COVID-19. Chest X-ray was performed in all hospitalized cases, while CT was available approximately in 30% of cases during hospital stay. The cases presented in the paper indicate potential advantages to the use of ultrasound in limited resource healthcare settings, especially when the risk of transportation to CT outweighs the value of information obtained. Conclusions: Grading of ultrasonographic findings in the lungs was sufficient for both initial assessment with identification of high risk patients, and routine daily monitoring. Hence, lung ultrsound may be used to predict deterioration, stratify risks and make clinical decisions.