Evidence map›Paper›PMID 36530356›Full record

ReviewFrontiers in artificial intelligence2022

A survey of COVID-19 detection and prediction approaches using mobile devices, AI, and telemedicine.

John Shen, Siddharth Ghatti, Nate Ryan Levkov, Haiying Shen, Tanmoy Sen, Karen Rheuban, Kyle Enfield, Nikki Reyer Facteau, Gina Engel, Kim Dowdell

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
1.7field-weighted citation impact, top 16% 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

1 citing paper in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.

  1. Pooled it
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

10 authors at 2 institutions in 1 country.

John ShenDepartment of Computer Science, University of Virginia, Charlottesville, VA, United States.
Siddharth GhattiDepartment of Computer Science, University of Virginia, Charlottesville, VA, United States.
Nate Ryan LevkovDepartment of Computer Science, University of Virginia, Charlottesville, VA, United States.
Haiying ShenDepartment of Computer Science, University of Virginia, Charlottesville, VA, United States.
Tanmoy SenDepartment of Computer Science, University of Virginia, Charlottesville, VA, United States.
Karen RheubanSchool of Medicine, University of Virginia, Charlottesville, VA, United States.
Kyle EnfieldSchool of Medicine, University of Virginia, Charlottesville, VA, United States.
Nikki Reyer FacteauUniversity of Virginia (UVA) Health System, University of Virginia, Charlottesville, VA, United States.
Gina EngelSchool of Medicine, University of Virginia, Charlottesville, VA, United States.
Kim DowdellSchool of Medicine, University of Virginia, Charlottesville, VA, United States.
University of Virginia · USUniversity of Virginia Health System · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since 2019, the COVID-19 pandemic has had an extremely high impact on all facets of the society and will potentially have an everlasting impact for years to come. In response to this, over the past years, there have been a significant number of research efforts on exploring approaches to combat COVID-19. In this paper, we present a survey of the current research efforts on using mobile Internet of Thing (IoT) devices, Artificial Intelligence (AI), and telemedicine for COVID-19 detection and prediction. We first present the background and then present current research in this field. Specifically, we present the research on COVID-19 monitoring and detection, contact tracing, machine learning based approaches, telemedicine, and security. We finally discuss the challenges and the future work that lay ahead in this field before concluding this paper.

Indexed as

AICOVID-19 detectionIoTmobile devicestelemedicine

Identifiers

PMID36530356
PMCPMC9755752
OpenAlexW4311111541

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.