APPLICATION OF ARTIFICIAL INTELLIGENCE IN PREDICTING THE CLINICAL COURSE OF COVID-19 PATIENTS

This thesis explores the application of artificial intelligence (AI) in predicting the clinical course of COVID-19 patients, with emphasis on diagnostic imaging, outcome prediction, and risk stratification. AI-based methods, including machine learning and deep learning, have demonstrated strong potential in identifying radiological patterns of viral pneumonia, forecasting patient deterioration, and supporting intensive care decision-making. Modern multimodal systems integrate clinical, laboratory, radiological, and demographic data, enabling more accurate and personalized prognostic models. The expanded text highlights new directions such as temporal modeling, federated learning, and resource optimization algorithms. Challenges remain, including limited annotated datasets, variability in data quality, and ethical considerations regarding clinical implementation. Despite these limitations, AI continues to play an increasingly important role in improving diagnostic accuracy, reducing delays in clinical decision-making, and enhancing healthcare delivery during pandemics.