A Comprehensive Review of Artificial Intelligence in Prevention and Treatment of COVID-19 Pandemic

Abstract

The unprecedented outbreak of the Corona Virus Disease 2019 (COVID-19) pandemic has seriously affected numerous countries in the world from various aspects such as education, economy, social security, public health, etc. Most governments have made great efforts to control the spread of COVID-19, e.g., locking down hard-hit cities and advocating masks for the population. However, some countries and regions have relatively poor medical conditions in terms of insufficient medical equipment, hospital capacity overload, personnel shortage, and other problems, resulting in the large-scale spread of the epidemic. With the unique advantages of Artificial Intelligence (AI), it plays an extremely important role in medical imaging, clinical data, drug development, epidemic prediction, and telemedicine. Therefore, AI is a powerful tool that can help humans solve complex problems, especially in the fight against COVID-19. This study aims to analyze past research results and interpret the role of Artificial Intelligence in the prevention and treatment of COVID-19 from five aspects. In this paper, we also discuss the future development directions in different fields and prove the validity of the models through experiments, which will help researchers develop more efficient models to control the spread of COVID-19.

Factors Related to Maternal Readiness at Telehealth in Prenatal Care in Rural Areas of Indonesia

“…Telehealth has also proven beneficial for inpatient care, specifically to help balance clinical service provision with surging demand across physical or geographic boundaries, conserving personal protective equipment, and providing remote patients connections to family and friends (20,21). Telehealth has several key strengths that can improve emergency response services when the environment is in a biological hazard and during infectious disease outbreaks (22). This research showed that there was no relationship between age and occupation factors on PNC telehealth readiness; meanwhile, there was a significant relationship between education and income with readiness in PNC telehealth.…”

A predictive decision support system for coronavirus disease 2019 response management and medical logistic planning

“… 32 Statistical models can rely on statistical regression models such as the Gaussian model, where the dynamics of the epidemic outbreaks are identified with time series of the identified infected cases. State space models, also referred to as traditional mathematical models, 33 describe the dynamics of the epidemic using differential equations by subdividing the population into several categories. These models include the susceptible-infected (SI) model, SIS model, susceptible-infected-recovered (SIR) model, the susceptible-exposed-infected-recovered (SEIR) model, etc.…”

“…Machine learning methods can include different types of models such as linear regression models, polynomial regression models, support vector machine learning, artificial neural network (ANN) models, etc. 33 …”

Artificial Intelligence: A Next-Level Approach in Confronting the COVID-19 Pandemic

“…Therefore, before the governments realized and initiated the containment measures, COVID-19 spread to various countries where people did not have self-protection awareness measures such as wearing masks, social distancing, etc. Countries with a limited healthcare infrastructure suffered more due to a lack of vaccines, systematic treatment, and elevated hospital bills that were not affordable [ 9 ]. Still, there is no effective antiviral drug; patients are treated with general treatment methods such as bed rest, ventilators, and conventional antibiotics.…”

“…Recently, Artificial Intelligence (AI) has received more attention due to its ability to observe, make decisions about, and interpret a huge volume of data in different fields [ 9 ], which include computer science, healthcare, drug discovery, engineering, infectious diseases, and government management, especially in this pandemic. Modern medicine and improvements in the healthcare sector have brought a huge amount of patient data related to medications.…”