The outbreak of COVID-19 put the whole world in an unprecedentedly harsh situation,
horribly disrupting life around the world and killing thousands. COVID-19 remains a real threat
to the public health system as it spreads to 212 countries and territories and the number of cases
of infection and deaths increases to 5,212,172 and 334,915 (as of May 22, 2020). This treatise
provides a response to virus eradication via artificial intelligence (AI). Several deep learning
(DL) methods have been described to achieve this goal, including GAN (Generative Adversarial
Network), ELM (Extreme Learning Machine), and LSTM (Long / Short Term Memory). It
describes an integrated bioinformatics approach that combines various aspects of information
from a series of orthopedic and unstructured data sources to form a user-friendly platform for
physicians and researchers. A major advantage of these AI-powered platforms is to facilitate the
diagnosis and treatment process of the COVID-19 disease. The latest relevant publications and
medical reports have been investigated to select inputs and targets for networks that will facilitate
arriving at reliable artificial neural network-based tools for COVID-19-related challenges. There
are also several specific inputs per platform, including clinical data and data in various formats,
such as medical images, which can improve the performance of the introduced method for the
best response in real application.