Dataset Information

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

ABSTRACT:

Objectives

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.

Study design

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.

Results

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).

Conclusion

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.

SUBMITTER: Tercero-Hidalgo JR

PROVIDER: S-EPMC9059390 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Publications

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

Tercero-Hidalgo Juan R JR Khan Khalid S KS Bueno-Cavanillas Aurora A Fernández-López Rodrigo R Huete Juan F JF Amezcua-Prieto Carmen C Zamora Javier J Fernández-Luna Juan M JM

Journal of clinical epidemiology 20220502

<h4>Objectives</h4>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.<h4>Study design</h4>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-rel ...[more]

PMID: 35513213

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Project description:Background: Recently, Coronavirus Disease 2019 (COVID-19), caused by severe acute respiratory syndrome virus 2 (SARS-CoV-2), has affected more than 200 countries and lead to enormous losses. This study systematically reviews the application of Artificial Intelligence (AI) techniques in COVID-19, especially for diagnosis, estimation of epidemic trends, prognosis, and exploration of effective and safe drugs and vaccines; and discusses the potential limitations. Methods: We report this systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched PubMed, Embase and the Cochrane Library from inception to 19 September 2020 for published studies of AI applications in COVID-19. We used PROBAST (prediction model risk of bias assessment tool) to assess the quality of literature related to the diagnosis and prognosis of COVID-19. We registered the protocol (PROSPERO CRD42020211555). Results: We included 78 studies: 46 articles discussed AI-assisted diagnosis for COVID-19 with total accuracy of 70.00 to 99.92%, sensitivity of 73.00 to 100.00%, specificity of 25 to 100.00%, and area under the curve of 0.732 to 1.000. Fourteen articles evaluated prognosis based on clinical characteristics at hospital admission, such as clinical, laboratory and radiological characteristics, reaching accuracy of 74.4 to 95.20%, sensitivity of 72.8 to 98.00%, specificity of 55 to 96.87% and AUC of 0.66 to 0.997 in predicting critical COVID-19. Nine articles used AI models to predict the epidemic of the COVID-19, such as epidemic peak, infection rate, number of infected cases, transmission laws, and development trend. Eight articles used AI to explore potential effective drugs, primarily through drug repurposing and drug development. Finally, 1 article predicted vaccine targets that have the potential to develop COVID-19 vaccines. Conclusions: In this review, we have shown that AI achieved high performance in diagnosis, prognosis evaluation, epidemic prediction and drug discovery for COVID-19. AI has the potential to enhance significantly existing medical and healthcare system efficiency during the COVID-19 pandemic.

Dataset Information

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

Objectives

Study design

Results

Conclusion

Publications

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

Similar Datasets

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