## 5 Conclusions  
In conclusion, the results obtained from the study were generally satisfactory, highlighting the potential for further development and refinement of the application. However, several areas require attention and improvement. Figure 11: Proposed therapeutic management of hospitalized adults with COVID-19 based on the COVID-19 severity score. Firstly, it is essential to acknowledge that the study utilized a limited dataset of medical images. Future research should consider incorporating different and more extensive databases to enhance the robustness and generalizability of the findings by providing a broader representation of cases. Moreover, the segmentation of the lungs may have unintentionally excluded relevant surrounding tissues and structures, such as tracheal tubes. Modifying the segmentation process and algorithm is crucial to ensure the inclusion of these structures and thus enhance the range of features used in the diagnostic model training. Additionally, the study only tested three neural network architectures for medical image classification in the diagnostic task. However, exploring a more comprehensive range of architectures may help identify potential superior alternatives. Different architectures offer distinct strengths and capabilities, potentially improving performance in medical image classification tasks. In terms of therapeutic suggestions, the current U-Net model provided outcomes in terms of the percentage involvement of pathological tissue within the total lung area on each CT slice. However, modifications of the model are required for a more objective and accurate analysis. Analyzing 3D reconstructions of the lungs derived from all CT slices of an individual patient's scan would allow for a more precise evaluation of the extent of the disease. Assessing the percentage involvement of pathologically altered tissue within the total lung volume would allow for the optimization of therapeutic suggestions, mitigating the risk of misdiagnosis associated with relying solely on a limited number of CT slices. Furthermore, constructing and training multiple models specifically for this task would facilitate a thorough performance comparison, enabling the identification of potential disadvantages associated with each model. This approach would make it possible to select the optimal model for accurate and personalized therapeutic suggestions. To further enhance patient care and management, it is crucial to consider the integration of the developed models with other diagnostic modalities and medical procedures beyond lung-related CT imaging. This integration would broaden the scope of clinical decisions for COVID-19 patients, enabling a comprehensive approach to diagnosis and treatment. Moreover, it is worth noting that the potential applications of machine learning extend beyond the scope of this study, encompassing various medical conditions and imaging modalities. As medical imaging devices continue to advance, providing improved visualization of previously undetectable features, the utilization of machine learning holds promise for further enhancing the quality of results and expanding its impact in the field of healthcare. AI integration in radiology holds promise for improving the early detection and diagnosis of malignancies. One significant advancement is the development of a deep learning model that can predict an individual's lung cancer risk up to six years in advance using a single low-dose computed tomography (LDCT) scan. This proactive approach enables personalized treatment plans, leading to improved patient outcomes [29]. The broader adoption of AI in radiology has the potential to revolutionize clinical practices by streamlining workflows and enhancing interpretative speed and accuracy. AI algorithms empower radiologists to deliver more precise and timely diagnoses, optimizing patient care [30; 31]. In conclusion, AI and deep learning solutions are transformative in radiology.