SynthesisJournal of the American Medical Informatics Association : JAMIA2022
Automated medical literature screening using artificial intelligence: a systematic review and meta-analysis.
Synthesis in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled 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.
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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming evidence synthesis: A systematic review of the evolution of automated meta-analysis in the age of AI.Research synthesis methods · 2026Pooled it
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Artificial Intelligence (AI) Readiness to Support Evidence Synthesis by Workflow: Findings From a Review of Reviews.Campbell systematic reviews · 2026Article
- Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review.Journal of medical Internet research · 2026Article
- Accelerating Evidence Synthesis: A BERT-Assisted Workflow for Meta-Analyses of Radiotherapy Complications in Nasopharyngeal Carcinoma.Reports (MDPI) · 2026Article
- Searching smarter, not harder: leveraging AI to enhance literature searches for theory-driven reviews-A methodological case study.BMC medical research methodology · 2026Article
- The landscape of artificial intelligence tools and platforms for evidence synthesis: a scoping review.Systematic reviews · 2026Article
- Assessing Large Language Models for Early Article Identification in Otolaryngology-Head and Neck Surgery Systematic Reviews.Health care science · 2026Article
- Article
- Using artificial intelligence for the development of a living evidence map: The pharmacopuncture example.Integrative medicine research · 2025Article
- Evaluation of semi-automated record screening methods for systematic reviews of prognosis studies and intervention studies.Research synthesis methods · 2025Article
- Article
- Artificial Intelligence and Automation in Evidence Synthesis: An Investigation of Methods Employed in Cochrane, Campbell Collaboration, and Environmental Evidence Reviews.Cochrane evidence synthesis and methods · 2025Article
- Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies For Literature Review Automation.Machine learning and knowledge extraction · 2025Article
- A comprehensive systematic review dataset is a rich resource for training and evaluation of AI systems for title and abstract screening.Research synthesis methods · 2025Article
- Evaluating GPT Models for Automated Literature Screening in Wastewater-Based Epidemiology.ACS environmental Au · 2025Article
- Article
- Peer Review in the Artificial Intelligence Era: A Call for Developing Responsible Integration Guidelines.Nature and science of sleep · 2025Article
- Concordance between humans and GPT-4 in appraising the methodological quality of case reports and case series using the Murad tool.BMC medical research methodology · 2024Article
- Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed.Systematic reviews · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
objectiveWe aim to investigate the application and accuracy of artificial intelligence (AI) methods for automated medical literature screening for systematic reviews. MATERIALS AND
methodsWe systematically searched PubMed, Embase, and IEEE Xplore Digital Library to identify potentially relevant studies. We included studies in automated literature screening that reported study question, source of dataset, and developed algorithm models for literature screening. The literature screening results by human investigators were considered to be the reference standard. Quantitative synthesis of the accuracy was conducted using a bivariate model.
resultsEighty-six studies were included in our systematic review and 17 studies were further included for meta-analysis. The combined recall, specificity, and precision were 0.928 [95% confidence interval (CI), 0.878-0.958], 0.647 (95% CI, 0.442-0.809), and 0.200 (95% CI, 0.135-0.287) when achieving maximized recall, but were 0.708 (95% CI, 0.570-0.816), 0.921 (95% CI, 0.824-0.967), and 0.461 (95% CI, 0.375-0.549) when achieving maximized precision in the AI models. No significant difference was found in recall among subgroup analyses including the algorithms, the number of screened literatures, and the fraction of included literatures. DISCUSSION AND
conclusionThis systematic review and meta-analysis study showed that the recall is more important than the specificity or precision in literature screening, and a recall over 0.95 should be prioritized. We recommend to report the effectiveness indices of automatic algorithms separately. At the current stage manual literature screening is still indispensable for medical systematic reviews.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.