ArticleJournal of clinical epidemiology2022
Artificial intelligence in COVID-19 evidence syntheses was underutilized, but impactful: a methodological study.
Article in Journal of clinical epidemiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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.
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.
Who cites it
13 citing papers in PubMed.
- Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews.Global epidemiology · 2026Article
- Efficacy of a Large Language Model Data Extraction System in Evidence Reviews for Emerging Infectious Diseases: A Randomized Crossover Trial.Open forum infectious diseases · 2026Article
- Evaluating the Methodological Quality of Artificial Intelligence-Assisted Systematic Reviews: Protocol for a Mixed Methods Meta-Research Study.JMIR research protocols · 2026Article
- Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review.Journal of medical Internet research · 2026Article
- The landscape of artificial intelligence tools and platforms for evidence synthesis: a scoping review.Systematic reviews · 2026Article
- Implementation of artificial intelligence (AI) in ASD treatment.North American Spine Society journal · 2025Article
- Artificial Intelligence and Machine Learning to Improve Evidence Synthesis Production Efficiency: An Observational Study of Resource Use and Time-to-Completion.Cochrane evidence synthesis and methods · 2025Article
- Utilizing Large language models to select literature for meta-analysis shows workload reduction while maintaining a similar recall level as manual curation.BMC medical research methodology · 2025Article
- Artificial intelligence in food and nutrition evidence: The challenges and opportunities.PNAS nexus · 2024Article
- A living critical interpretive synthesis to yield a framework on the production and dissemination of living evidence syntheses for decision-making.Implementation science : IS · 2024Review
- Assessing the Integrity of Clinical Trials Included in Evidence Syntheses.International journal of environmental research and public health · 2023Review
- Publications on COVID-19 in radiology journals in 2020 and 2021: bibliometric citation and co-citation network analysis.European radiology · 2023Article
- Characteristics of Living Systematic Review for COVID-19.Clinical epidemiology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesA 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
designAfter 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.
resultsOf 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).
conclusionAI 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.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.