Evidence map›Paper›PMID 34036497›Full record

ReviewEnvironmental science and pollution research international2021

Artificial intelligence as a fundamental tool in management of infectious diseases and its current implementation in COVID-19 pandemic.

Ishnoor Kaur, Tapan Behl, Lotfi Aleya, Habibur Rahman, Arun Kumar, Sandeep Arora, Israt Jahan Bulbul

Abstract readReview
In one paragraph

Review in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
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

14 citing papers in PubMed.

  1. Article
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  3. AI in humanitarian healthcare: a game changer for crisis response.Frontiers in artificial intelligence · 2025
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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.

Ishnoor KaurChitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India.
Tapan BehlChitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India. tapanbehl31@gmail.com.ORCID http://orcid.org/0000-0003-0993-7708
Lotfi AleyaChrono-Environment Laboratory, UMR CNRS 6249, Bourgogne Franche-Comté University, Besançon, France.
Habibur RahmanDepartment of Global Medical Science, Wonju College of Medicine, Yonsei University, Seoul, South Korea.
Arun KumarChitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India.
Sandeep AroraChitkara College of Pharmacy, Chitkara University, Chandigarh, Punjab, India.
Israt Jahan BulbulDepartment of Pharmacy, Southeast University, Banani, Dhaka, 1213, Bangladesh.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The world has never been prepared for global pandemics like the COVID-19, currently posing an immense threat to the public and consistent pressure on the global healthcare systems to navigate optimized tools, equipments, medicines, and techno-driven approaches to retard the infection spread. The synergized outcome of artificial intelligence paradigms and human-driven control measures elicit a significant impact on screening, analysis, prediction, and tracking the currently infected individuals, and likely the future patients, with precision and accuracy, generating regular international and national data on confirmed, recovered, and death cases, as the current status of 3,820,869 infected patients worldwide. Artificial intelligence is a frontline concept, with time-saving, cost-effective, and productive access to disease management, rendering positive results in physician assistance in high workload conditions, radiology imaging, computational tomography, and database formulations, to facilitate availability of information accessible to researchers all over the globe. The review tends to elaborate the role of industry 4.0 technology, fast diagnostic procedures, and convolutional neural networks, as artificial intelligence aspects, in potentiating the COVID-19 management criteria and differentiating infection in SARS-CoV-2 positive and negative groups. Therefore, the review successfully supplements the processes of vaccine development, disease management, diagnosis, patient records, transmission inhibition, social distancing, and future pandemic predictions, with artificial intelligence revolution and smart techno processes to ensure that the human race wins this battle with COVID-19 and many more combats in the future.

Indexed as

Communicable DiseasesCOVID-19Artificial IntelligenceHumansPandemicsSARS-CoV-2Computational tomographyCOVID-19Disease managementIndustry 4.0Radiology imagingTechno-driven

Identifiers

PMID34036497
PMCPMC8148397

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.