COVID-19 and Radiology: Lessons Learned and Future Directions

Abstract

Background: The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has significantly impacted global health, necessitating rapid advancements in diagnostic methods. Radiology, particularly through the use of Artificial Intelligence (AI), has emerged as a critical tool in the early detection and management of COVID-19.

Methods: This review examines the application of AI-driven machine learning (ML) and deep learning (DL) techniques in the analysis of medical imaging, specifically chest X-rays and computed tomography (CT) scans, to diagnose COVID-19. We assessed various studies that implemented these technologies, focusing on their methodologies, accuracy, and diagnostic capabilities.

Results: The findings indicate that AI algorithms can analyze imaging data with remarkable speed and accuracy, achieving sensitivity rates comparable to experienced radiologists. For instance, deep learning models demonstrated accuracy levels exceeding 90% in identifying COVID-19 pneumonia from CT scans. Additionally, AI has facilitated the development of predictive models for disease severity, aiding clinical decision-making during the pandemic.

Conclusion: The integration of AI in radiological practices has proven to be a game-changer in the fight against COVID-19, enhancing diagnostic efficiency and accuracy. However, challenges such as data quality, algorithm interpretability, and the need for standardized protocols remain. Future research should focus on refining these technologies and ensuring their clinical applicability to better prepare for potential future health crises.