The therapeutic value of artificial intelligence (ML) in the diagnosis of viral
illnesses has been illustrated by the outbreak of COVID-19. This chapter digs into the
modern uses of Artificial Intelligence and Machine Learning (ML) algorithms for
COVID-19 diagnosis, with a focus on chest imaging procedures like as CT and X-rays.
Additionally, we explored ML's strengths, such as its capacity to analyze enormous
datasets and detect patterns in medical imagery. But there are still issues to deal with,
like the scarcity of data, privacy issues, and machine learning's incapacity to evaluate
the severity of health conditions. However, several machine learning methods, such as
decision trees, random forests, and convolutional neural networks, are reviewed in this
research concerning COVID-19 diagnosis. Subsequently, we highlight the efficacy of
several models in COVID-19 screening, such as XGBoost and Truncated Inception
Net. Moreover, the chapter discusses potential strategies for machine learning in
COVID-19 diagnosis, emphasizing the crucial role of collaboration among data
scientists and healthcare experts. It is imperative to confront data bias and incorporate
more comprehensive patient data than just chest imaging. All things considered,
machine learning presents a potential pathway toward quick and precise COVID-19
diagnosis; nonetheless, conquering existing obstacles is necessary for ML to be widely
used in healthcare institutions.
Keywords: Artificial Intelligence (AI), COVID-19, Chest X-ray, Computed Tomography (CT), Computer-aided Diagnosis (CAD), Deep Learning (DL), Diagnostic Imaging, Ground-Glass Opacity (GGO), Machine Learning (ML), Radiological Analysis, Variants of Concern (VOC).
