  Background: This paper provides a systematic review of the application of
Artificial Intelligence (AI) in the form of Machine Learning (ML) and Deep
Learning (DL) techniques in fighting against the effects of novel coronavirus
disease (COVID-19). Objective & Methods: The objective is to perform a scoping
review on AI for COVID-19 using preferred reporting items of systematic reviews
and meta-analysis (PRISMA) guidelines. A literature search was performed for
relevant studies published from 1 January 2020 till 27 March 2021. Out of 4050
research papers available in reputed publishers, a full-text review of 440
articles was done based on the keywords of AI, COVID-19, ML, forecasting, DL,
X-ray, and Computed Tomography (CT). Finally, 52 articles were included in the
result synthesis of this paper. As part of the review, different ML regression
methods were reviewed first in predicting the number of confirmed and death
cases. Secondly, a comprehensive survey was carried out on the use of ML in
classifying COVID-19 patients. Thirdly, different datasets on medical imaging
were compared in terms of the number of images, number of positive samples and
number of classes in the datasets. The different stages of the diagnosis,
including preprocessing, segmentation and feature extraction were also
reviewed. Fourthly, the performance results of different research papers were
compared to evaluate the effectiveness of DL methods on different datasets.
Results: Results show that residual neural network (ResNet-18) and densely
connected convolutional network (DenseNet 169) exhibit excellent classification
accuracy for X-ray images, while DenseNet-201 has the maximum accuracy in
classifying CT scan images. This indicates that ML and DL are useful tools in
assisting researchers and medical professionals in predicting, screening and
detecting COVID-19.
