Evidence map›Paper›PMID 36981511›Full record

ReviewHealthcare (Basel, Switzerland)2023

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

V Mahalakshmi, Awatef Balobaid, B Kanisha, R Sasirekha, M Ramkumar Raja

Open access · goldFull text readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 13% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Review
  2. Patient Autonomy in Medical Education: Navigating Ethical Challenges in the Age of Artificial Intelligence.Inquiry : a journal of medical care organization, provision and financing
    Review
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

5 authors at 3 institutions in 2 countries.

V MahalakshmiDepartment of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan 45142, Saudi Arabia.
Awatef BalobaidDepartment of Computer Science, College of Computer Science & Information Technology, Jazan University, Jazan 45142, Saudi Arabia.
B KanishaDepartment of Computer Science and Engineering, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Chengalpattu 603203, India.ORCID 0000-0001-6942-2576
R SasirekhaDepartment of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur Campus, Chengalpattu 603203, India.ORCID 0000-0002-1956-3095
M Ramkumar RajaDepartment of Electrical Engineering, College of Engineering, King Khalid University, Abha 62529, Saudi Arabia.
Jazan University · SASRM Institute of Science and Technology · INKing Khalid University · SA

Funding

This research was funded by the Deanship of Scientific Research at King Khalid University, Kingdom of Saudi Arabia RGP.1 / 236/43.
6 · The paper itself

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which caused coronavirus diseases (COVID-19) in late 2019 in China created a devastating economical loss and loss of human lives. To date, 11 variants have been identified with minimum to maximum severity of infection and surges in cases. Bacterial co-infection/secondary infection is identified during viral respiratory infection, which is a vital reason for morbidity and mortality. The occurrence of secondary infections is an additional burden to the healthcare system; therefore, the quick diagnosis of both COVID-19 and secondary infections will reduce work pressure on healthcare workers. Therefore, well-established support from Artificial Intelligence (AI) could reduce the stress in healthcare and even help in creating novel products to defend against the coronavirus. AI is one of the rapidly growing fields with numerous applications for the healthcare sector. The present review aims to access the recent literature on the role of AI and how its subfamily machine learning (ML) and deep learning (DL) are used to curb the pandemic's effects. We discuss the role of AI in COVID-19 infections, the detection of secondary infections, technology-assisted protection from COVID-19, global laws and regulations on AI, and the impact of the pandemic on public life.

Indexed as

AI lawartificial intelligencecoronavirusCOVID-19face masknanofiber mask

Identifiers

PMID36981511
PMCPMC10048108
OpenAlexW4324142276

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read8
measurements read78
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.