ArticleBMC medical research methodology2021
Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses.
Article in BMC medical research methodology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers, 6 of them syntheses 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
49 citing papers in PubMed, 6 syntheses or guidelines pooled it.
- Exercise and pathologic complete response to cancer treatment: a systematic review and meta-analysis.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026Pooled it
- Systematic Review of Growth in Young Children With Cleft Lip and/or Cleft Palate From 0 to 24 Months of Age.The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2026Pooled it
- Risk of new diagnoses and exacerbations of chronic conditions after SARS-CoV-2 infection: a systematic review update.Epidemiologic reviews · 2026Pooled it
- Type 2 Diabetes Remission: A Systematic Review and Meta-analysis of Nonsurgical Randomized Controlled Trials.Diabetes care · 2025Pooled it
- Agreement Between Heart Rate Variability - Derived vs. Ventilatory and Lactate Thresholds: A Systematic Review with Meta-Analyses.Sports medicine - open · 2024Pooled it
- Characteristics and recovery methods of studies falsely excluded during literature screening-a systematic review.Systematic reviews · 2022Pooled it
- Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews.Global epidemiology · 2026Article
- Reprioritising consultation in scoping reviews: Clarifying purposes and practices.Medical education · 2026Article
- Application of Machine Learning Classifiers in Rapid Reviews for Health Research: A Case Example Using EPPI-Reviewer.Cochrane evidence synthesis and methods · 2026Article
- Birth Weight in Infants with a Cleft Lip and/or Palate: A Systematic Review and Meta-Analysis.The Journal of pediatrics · 2026Article
- Evaluating the Methodological Quality of Artificial Intelligence-Assisted Systematic Reviews: Protocol for a Mixed Methods Meta-Research Study.JMIR research protocols · 2026Article
- Comparing the performance of narrow vs. broad search strategies when using machine learning-based software for title/abstract screening.Journal of the Medical Library Association : JMLA · 2026Article
- Screenathon 2.0: human-AI collaborative screening applied to patient-generated health data.Scientific reports · 2026Article
- Capability of chatbots powered by large language models to support the screening process of scoping reviews: a feasibility study.JAMIA open · 2026Article
- Extraction, analysis and presentation of results in a scoping review in Brazilian nursing: scoping review.Revista latino-americana de enfermagem · 2026Article
- Harnessing ChatGPT for abstract screening in health-related scoping reviews: the role of structured eligibility criteria.BMC health services research · 2025Article
- Protocol: Understanding the Content, Context, and Impact of Far-Right Extremist Propaganda Disseminated Online: A Systematic Review.Campbell systematic reviews · 2025Article
- How Do Trauma- and Violence-Informed Care Approaches Underpin Bariatric Surgery Interventions for Type 2 Diabetes Mellitus Remission? A Scoping Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Article
- Enhancing Evidence Synthesis Efficiency: Leveraging Large Language Models and Agentic Workflows for Optimized Literature Screening.Cochrane evidence synthesis and methods · 2025Article
- Missing the Target: A Scoping Review of the Use of Percent Weight Loss for Obesity Management.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2025Article
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
Abstract
backgroundSystematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided different situations and considerations for knowledge synthesis teams to consider when using artificial intelligence and AML for title and abstract screening.
methodsWe retrospectively evaluated the implementation and performance of AML across a set of ten historically completed systematic reviews. Based upon the findings from this work and in consideration of the barriers we have encountered and navigated during the past 24 months in using these tools prospectively in our research, we discussed and developed a series of practical recommendations for research teams to consider in seeking to implement AML tools for citation screening into their workflow.
resultsWe developed a seven-step framework and provide guidance for when and how to integrate artificial intelligence and AML into the title and abstract screening process. Steps include: (1) Consulting with Knowledge user/Expert Panel; (2) Developing the search strategy; (3) Preparing your review team; (4) Preparing your database; (5) Building the initial training set; (6) Ongoing screening; and (7) Truncating screening. During Step 6 and/or 7, you may also choose to optimize your team, by shifting some members to other review stages (e.g., full-text screening, data extraction).
conclusionArtificial intelligence and, more specifically, AML are well-developed tools for title and abstract screening and can be integrated into the screening process in several ways. Regardless of the method chosen, transparent reporting of these methods is critical for future studies evaluating artificial intelligence and AML.
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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.