Publication:
Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study.

dc.contributor.authorTercero-Hidalgo, Juan R
dc.contributor.authorKhan, Khalid S
dc.contributor.authorBueno-Cavanillas, Aurora
dc.contributor.authorFernandez-Lopez, Rodrigo
dc.contributor.authorHuete, Juan F
dc.contributor.authorAmezcua-Prieto, Carmen
dc.contributor.authorZamora, Javier
dc.contributor.authorFernandez-Luna, Juan M
dc.date.accessioned2023-05-03T15:03:17Z
dc.date.available2023-05-03T15:03:17Z
dc.date.issued2022-04-28
dc.description.abstractA rapidly developing scenario like a pandemic requires the prompt production of high-quality systematic reviews, which can be automated using artificial intelligence (AI) techniques. We evaluated the application of AI tools in COVID-19 evidence syntheses. After prospective registration of the review protocol, we automated the download of all open-access COVID-19 systematic reviews in the COVID-19 Living Overview of Evidence database, indexed them for AI-related keywords, and located those that used AI tools. We compared their journals' JCR Impact Factor, citations per month, screening workloads, completion times (from pre-registration to preprint or submission to a journal) and AMSTAR-2 methodology assessments (maximum score 13 points) with a set of publication date matched control reviews without AI. Of the 3,999 COVID-19 reviews, 28 (0.7%, 95% CI 0.47-1.03%) made use of AI. On average, compared to controls (n = 64), AI reviews were published in journals with higher Impact Factors (median 8.9 vs. 3.5, P  AI was an underutilized tool in COVID-19 systematic reviews. Its usage, compared to reviews without AI, was associated with more efficient screening of literature and higher publication impact. There is scope for the application of AI in automating systematic reviews.
dc.description.versionSi
dc.identifier.citationTercero-Hidalgo JR, Khan KS, Bueno-Cavanillas A, Fernández-López R, Huete JF, Amezcua-Prieto C, et al. Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study. J Clin Epidemiol. 2022 Aug;148:124-134.
dc.identifier.doi10.1016/j.jclinepi.2022.04.027
dc.identifier.essn1878-5921
dc.identifier.pmcPMC9059390
dc.identifier.pmid35513213
dc.identifier.pubmedURLhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9059390/pdf
dc.identifier.unpaywallURLhttp://www.jclinepi.com/article/S0895435622001160/pdf
dc.identifier.urihttp://hdl.handle.net/10668/22294
dc.journal.titleJournal of clinical epidemiology
dc.journal.titleabbreviationJ Clin Epidemiol
dc.language.isoen
dc.organizationInstituto de Investigación Biosanitaria de Granada (ibs.GRANADA)
dc.page.number124-134
dc.publisherElsevier Inc.
dc.pubmedtypeJournal Article
dc.pubmedtypeResearch Support, Non-U.S. Gov't
dc.relation.publisherversionhttps://linkinghub.elsevier.com/retrieve/pii/S0895-4356(22)00116-0
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.accessRightsopen access
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectArtificial intelligence
dc.subjectAutomation
dc.subjectBibliometrics
dc.subjectCOVID-19
dc.subjectResearch design
dc.subjectSystematic review
dc.subject.decsEstudios prospectivos
dc.subject.decsFactor de impacto de la revista
dc.subject.decsHumanos
dc.subject.decsInteligencia artificial
dc.subject.decsPandemias
dc.subject.meshHumans
dc.subject.meshCOVID-19
dc.subject.meshArtificial Intelligence
dc.subject.meshProspective Studies
dc.subject.meshPandemics
dc.subject.meshJournal Impact Factor
dc.titleArtificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study.
dc.typeresearch article
dc.type.hasVersionVoR
dc.volume.number148
dspace.entity.typePublication

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