Artificial Intelligence in Covid-19 Management: A Systematic Review Publisher



Mohammadi S^{1, 2} ; Alinaghi SAS² ; Heydari M³ ; Pashaei Z^{2, 4} ; Mirzapour P² ; Karimi A⁵ ; Afsahi AM⁶ ; Mirghaderi P⁷ ; Mohammadi P⁵ ; Arjmand G⁸ ; Soleimani Y⁹ ; Azarnoush A¹⁰ ; Mojdeganlou H¹¹ ; Dashti M¹² Show All Authors Show Affiliations
Source: Journal of Computer Science Published:2023

Abstract

With the development of modern technologies in the field of healthcare, the use of Artificial Intelligence (AI) in disease management is increasing. AI methods may assist healthcare providers in the COVID-19 era. The current study aimed to observe the efficacy and importance of AI for managing the COVID-19 pandemic. An organized search was conducted, utilizing PubMed, Web of Science, Scopus, Embase, and Cochrane up to September 2022. Studies were considered qualified for inclusion if they met the inclusion criterion. We conducted review according to the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) guidelines. There were 52 documents that met the eligibility criteria to be included in the review. The most common item using AI during the COVID-19 era was predictive models to foretell pneumonia and mortality risks in people with COVID-19 based on medical and experimental parameters. COVID-19 mortality was related to being male and elderly based on the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) logistic regression analysis of demographics, clinical data, and laboratory tests of hospitalized COVID-19 patients. AI can predict, diagnose and model COVID-19 by using techniques such as support vector machines, decision trees, and neural networks. It is suggested that future research should deal with the design and development of AI-based tools for the management of chronic diseases such as COVID-19. © 2023 Samaneh Mohammadi, SeyedAhmad SeyedAlinaghi, Mohammad Heydari, Zahra Pashaei, Pegah Mirzapour, Amirali Karimi, Amir Masoud Afsahi, Peyman Mirghaderi, Parsa Mohammadi, Ghazal Arjmand, Yasna Soleimani, Ayein Azarnoush, Hengameh Mojdeganlou, Mohsen Dashti, Hadiseh Azadi Cheshmekabodi, Sanaz Varshochi, Mohammad Mehrtak, Ahmadreza Shamsabadi, Esmaeil Mehraeen, and Daniel Hackett. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.