Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study
A 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...
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| Veröffentlicht in: | Journal of clinical epidemiology 2022-08, Vol.148, p.124-134 |
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| creator | Tercero-Hidalgo, Juan R. Khan, Khalid S. Bueno-Cavanillas, Aurora Fernández-López, Rodrigo Huete, Juan F. Amezcua-Prieto, Carmen Zamora, Javier Fernández-Luna, Juan M. |
| description | A 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 |
| doi_str_mv | 10.1016/j.jclinepi.2022.04.027 |
| format | Article |
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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 < 0.001), and screened more abstracts per author (302.2 vs. 140.3, P = 0.009) and per included study (189.0 vs. 365.8, P < 0.001) while inspecting less full texts per author (5.3 vs. 14.0, P = 0.005). No differences were found in citation counts (0.5 vs. 0.6, P = 0.600), inspected full texts per included study (3.8 vs. 3.4, P = 0.481), completion times (74.0 vs. 123.0, P = 0.205) or AMSTAR-2 (7.5 vs. 6.3, P = 0.119).
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.
[Display omitted]</description><identifier>ISSN: 0895-4356</identifier><identifier>EISSN: 1878-5921</identifier><identifier>DOI: 10.1016/j.jclinepi.2022.04.027</identifier><identifier>PMID: 35513213</identifier><language>eng</language><publisher>United States: Elsevier Inc</publisher><subject>Artificial Intelligence ; Automation ; Bias ; Bibliometrics ; Citation analysis ; Citations ; Coronaviruses ; COVID-19 ; COVID-19 - epidemiology ; Covid-19 Series ; Epidemiology ; Funding ; Humans ; Impact factors ; Journal Impact Factor ; Keywords ; Literature reviews ; Pandemics ; Prospective Studies ; Research design ; Reviews ; Screening ; Systematic review ; Texts ; Workloads</subject><ispartof>Journal of clinical epidemiology, 2022-08, Vol.148, p.124-134</ispartof><rights>2022 The Authors</rights><rights>Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.</rights><rights>2022. The Authors</rights><rights>2022 The Authors 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c499t-81293eaeae9c438474037682182caebd7968469e29784afec55b182c42df82403</citedby><cites>FETCH-LOGICAL-c499t-81293eaeae9c438474037682182caebd7968469e29784afec55b182c42df82403</cites><orcidid>0000-0003-4526-4379</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/2727189432?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>230,315,781,785,886,3551,27929,27930,46000,64390,64392,64394,72474</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35513213$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tercero-Hidalgo, Juan R.</creatorcontrib><creatorcontrib>Khan, Khalid S.</creatorcontrib><creatorcontrib>Bueno-Cavanillas, Aurora</creatorcontrib><creatorcontrib>Fernández-López, Rodrigo</creatorcontrib><creatorcontrib>Huete, Juan F.</creatorcontrib><creatorcontrib>Amezcua-Prieto, Carmen</creatorcontrib><creatorcontrib>Zamora, Javier</creatorcontrib><creatorcontrib>Fernández-Luna, Juan M.</creatorcontrib><title>Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study</title><title>Journal of clinical epidemiology</title><addtitle>J Clin Epidemiol</addtitle><description>A 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 < 0.001), and screened more abstracts per author (302.2 vs. 140.3, P = 0.009) and per included study (189.0 vs. 365.8, P < 0.001) while inspecting less full texts per author (5.3 vs. 14.0, P = 0.005). No differences were found in citation counts (0.5 vs. 0.6, P = 0.600), inspected full texts per included study (3.8 vs. 3.4, P = 0.481), completion times (74.0 vs. 123.0, P = 0.205) or AMSTAR-2 (7.5 vs. 6.3, P = 0.119).
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.
[Display omitted]</description><subject>Artificial Intelligence</subject><subject>Automation</subject><subject>Bias</subject><subject>Bibliometrics</subject><subject>Citation analysis</subject><subject>Citations</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 - epidemiology</subject><subject>Covid-19 Series</subject><subject>Epidemiology</subject><subject>Funding</subject><subject>Humans</subject><subject>Impact factors</subject><subject>Journal Impact Factor</subject><subject>Keywords</subject><subject>Literature reviews</subject><subject>Pandemics</subject><subject>Prospective Studies</subject><subject>Research design</subject><subject>Reviews</subject><subject>Screening</subject><subject>Systematic review</subject><subject>Texts</subject><subject>Workloads</subject><issn>0895-4356</issn><issn>1878-5921</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqFkU1v1DAQhi0EokvhL1SRuHAgwXac2OaAWi1flSr1Alwtrz3ZdZTEi-0sWn49DttWwAX5MLLnmdcz8yJ0QXBFMGnf9FVvBjfB3lUUU1phVmHKH6EVEVyUjaTkMVphIZuS1U17hp7F2GNMOObNU3RWNw2pKalXyF-F5DpnnB4KNyUYBreFyUC-FOvbb9fvSyILODj7-zEep7SDCLH4oWMxTxbCnNzgfoJ9XWzmVLhxr03q5uFtoYsR0s5bP_itM1k-ptken6MnnR4ivLiL5-jrxw9f1p_Lm9tP1-urm9IwKVMpCJU16HykYbVgnOGat4ISQY2GjeWyFayVQCUXTHdgmmaz5Bi1naAZPkfvTrr7eTOCNTCloAe1D27U4ai8durvzOR2ausPSuJG1nIReHUnEPz3GWJSo4sm70dP4OeoaNsSLIRgNKMv_0F7P4cpj6cop5wIyeqFak-UCT7GAN1DMwSrxVPVq3tP1eKpwkxlT3PhxZ-jPJTdm5iByxMAeaEHB0FF4xa_rAtgkrLe_e-PX-pWt1w</recordid><startdate>20220801</startdate><enddate>20220801</enddate><creator>Tercero-Hidalgo, Juan R.</creator><creator>Khan, Khalid S.</creator><creator>Bueno-Cavanillas, Aurora</creator><creator>Fernández-López, Rodrigo</creator><creator>Huete, Juan F.</creator><creator>Amezcua-Prieto, Carmen</creator><creator>Zamora, Javier</creator><creator>Fernández-Luna, Juan M.</creator><general>Elsevier Inc</general><general>Elsevier Limited</general><general>The Authors. 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of clinical epidemiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tercero-Hidalgo, Juan R.</au><au>Khan, Khalid S.</au><au>Bueno-Cavanillas, Aurora</au><au>Fernández-López, Rodrigo</au><au>Huete, Juan F.</au><au>Amezcua-Prieto, Carmen</au><au>Zamora, Javier</au><au>Fernández-Luna, Juan M.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study</atitle><jtitle>Journal of clinical epidemiology</jtitle><addtitle>J Clin Epidemiol</addtitle><date>2022-08-01</date><risdate>2022</risdate><volume>148</volume><spage>124</spage><epage>134</epage><pages>124-134</pages><issn>0895-4356</issn><eissn>1878-5921</eissn><abstract>A 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 < 0.001), and screened more abstracts per author (302.2 vs. 140.3, P = 0.009) and per included study (189.0 vs. 365.8, P < 0.001) while inspecting less full texts per author (5.3 vs. 14.0, P = 0.005). No differences were found in citation counts (0.5 vs. 0.6, P = 0.600), inspected full texts per included study (3.8 vs. 3.4, P = 0.481), completion times (74.0 vs. 123.0, P = 0.205) or AMSTAR-2 (7.5 vs. 6.3, P = 0.119).
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.
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| ispartof | Journal of clinical epidemiology, 2022-08, Vol.148, p.124-134 |
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| source | MEDLINE; Access via ScienceDirect (Elsevier); ProQuest Central UK/Ireland |
| subjects | Artificial Intelligence Automation Bias Bibliometrics Citation analysis Citations Coronaviruses COVID-19 COVID-19 - epidemiology Covid-19 Series Epidemiology Funding Humans Impact factors Journal Impact Factor Keywords Literature reviews Pandemics Prospective Studies Research design Reviews Screening Systematic review Texts Workloads |
| title | Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study |
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