Evidence map›Paper›PMID 34400854›Full record

ReviewExpert systems with applications2021

Applications of artificial intelligence in COVID-19 pandemic: A comprehensive review.

Muzammil Khan, Muhammad Taqi Mehran, Zeeshan Ul Haq, Zahid Ullah, Salman Raza Naqvi, Mehreen Ihsan, Haider Abbass

Open access · greenAbstract readReview
In one paragraph

Review in Expert systems with applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 3 pooled it
22.6field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

53 citing papers in PubMed, 3 syntheses or guidelines pooled it, 196 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Muzammil KhanSchool of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan.
Muhammad Taqi MehranSchool of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan.
Zeeshan Ul HaqSchool of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan.
Zahid UllahSchool of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan.
Salman Raza NaqviSchool of Chemical & Materials Engineering, National University of Sciences & Technology, H-12, Islamabad 44000, Pakistan.
Mehreen IhsanPeshawar Medical College, Peshawar, Khyber Pakhtunkhwa 25000, Pakistan.
Haider AbbassNational Cyber Security Auditing and Evaluation LAb, National University of Sciences & Technology, MCS Campus, Rawalpindi 43600, Pakistan.
National University of Sciences and Technology · PKKhyber Medical College · PK

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

During the current global public health emergency caused by novel coronavirus disease 19 (COVID-19), researchers and medical experts started working day and night to search for new technologies to mitigate the COVID-19 pandemic. Recent studies have shown that artificial intelligence (AI) has been successfully employed in the health sector for various healthcare procedures. This study comprehensively reviewed the research and development on state-of-the-art applications of artificial intelligence for combating the COVID-19 pandemic. In the process of literature retrieval, the relevant literature from citation databases including ScienceDirect, Google Scholar, and Preprints from arXiv, medRxiv, and bioRxiv was selected. Recent advances in the field of AI-based technologies are critically reviewed and summarized. Various challenges associated with the use of these technologies are highlighted and based on updated studies and critical analysis, research gaps and future recommendations are identified and discussed. The comparison between various machine learning (ML) and deep learning (DL) methods, the dominant AI-based technique, mostly used ML and DL methods for COVID-19 detection, diagnosis, screening, classification, drug repurposing, prediction, and forecasting, and insights about where the current research is heading are highlighted. Recent research and development in the field of artificial intelligence has greatly improved the COVID-19 screening, diagnostics, and prediction and results in better scale-up, timely response, most reliable, and efficient outcomes, and sometimes outperforms humans in certain healthcare tasks. This review article will help researchers, healthcare institutes and organizations, government officials, and policymakers with new insights into how AI can control the COVID-19 pandemic and drive more research and studies for mitigating the COVID-19 outbreak.

Indexed as

Artificial intelligenceCOVID-19Deep learningDrug repurposingMachine learningSARS-CoV-2

Identifiers

PMID34400854
PMCPMC8359727
OpenAlexW3189284249

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.