Evidence map›Paper›PMID 34300099›Full record

ReviewInternational journal of environmental research and public health2021

How Artificial Intelligence and New Technologies Can Help the Management of the COVID-19 Pandemic.

Davide Barbieri, Enrico Giuliani, Anna Del Prete, Amanda Losi, Matteo Villani, Alberto Barbieri

Open access · goldAbstract readReview
In one paragraph

Review in International journal of environmental research and public health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
2.4field-weighted citation impact, top 11% 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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
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

6 authors at 3 institutions in 1 country.

Davide BarbieriDepartment of Neuroscience and Rehabilitation, University of Ferrara, Via Savonarola 9, 44121 Ferrara, Italy.
Enrico GiulianiDepartment of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy.
Anna Del PreteSchool of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy.ORCID 0000-0003-4424-9855
Amanda LosiDepartment of Biomedical, Metabolic and Neuroscience Sciences, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy.
Matteo VillaniDepartment of Anesthesiology and Intensive Care, Azienda USL Piacenza, Via Antonio Anguissola 15, 29121 Piacenza, Italy.ORCID 0000-0003-4269-6096
Alberto BarbieriSchool of Anesthesiology and Intensive Care, University of Modena and Reggio Emilia, Via Del Pozzo 71, 41125 Modena, Italy.
University of Modena and Reggio Emilia · ITOspedaliera di Piacenza · ITUniversity of Ferrara · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has worked as a catalyst, pushing governments, private companies, and healthcare facilities to design, develop, and adopt innovative solutions to control it, as is often the case when people are driven by necessity. After 18 months since the first case, it is time to think about the pros and cons of such technologies, including artificial intelligence-which is probably the most complex and misunderstood by non-specialists-in order to get the most out of them, and to suggest future improvements and proper adoption. The aim of this narrative review was to select the relevant papers that directly address the adoption of artificial intelligence and new technologies in the management of pandemics and communicable diseases such as SARS-CoV-2: environmental measures; acquisition and sharing of knowledge in the general population and among clinicians; development and management of drugs and vaccines; remote psychological support of patients; remote monitoring, diagnosis, and follow-up; and maximization and rationalization of human and material resources in the hospital environment.

Indexed as

COVID-19PandemicsArtificial IntelligenceHumansSARS-CoV-2Technologyartificial intelligenceCOVID-19new technologiespandemic management

Identifiers

PMID34300099
PMCPMC8303245
OpenAlexW3185363136

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.