ArticleBMC medical research methodology2024
Methodological insights into ChatGPT's screening performance in systematic reviews.
Article in BMC medical research methodology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 5 of them syntheses that pooled it.
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Who cites it
39 citing papers in PubMed, 5 syntheses or guidelines 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
- Tools for dementia disclosure: a systematic review of -family caregivers' perspectives and experiences.The Gerontologist · 2026Pooled it
- A comparative study of screening performance between abstrackr and GPT models: Systematic review and contextual analysis.BMC medical informatics and decision making · 2025Pooled it
- Generative artificial intelligence use in evidence synthesis: A systematic review.Research synthesis methods · 2025Pooled it
- The emergence of large language models as tools in literature reviews: a large language model-assisted systematic review.Journal of the American Medical Informatics Association : JAMIA · 2025Pooled it
- Developing and Evaluating the Use of ChatGPT as a Screening Tool for Nurses Conducting Structured Literature Reviews: Proof of Concept Study Results.Journal of clinical nursing · 2026Article
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Artificial intelligence-driven study selection in systematic reviews of randomized controlled trials, emulated trials and economic evaluation studies using large language models.PLOS digital health · 2026Article
- Article
- Evaluating the Utility of Artificial Intelligence in Conducting Systematic Reviews.Arthroplasty today · 2026Article
- Evaluating Elicit's systematic reviews workflow in an umbrella review on air pollution and acute lower respiratory infections: a methodological study for quality appraisal.BMC medical research methodology · 2026Article
- From Metabolomics to Function: Ranking Plant Stem Cell Metabolomes for Use in Health and Cosmetics.Biomolecules · 2026Article
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- Compact large language models for title and abstract screening in systematic reviews: An assessment of feasibility, accuracy, and workload reduction.Research synthesis methods · 2026Article
- Machine learning-assisted abstract screening on learning analytics: a step-by-step tutorial.Systematic reviews · 2026Article
- Large language models in systematic review and meta-analysis of surgical treatments for vaginal vault prolapse.NPJ digital medicine · 2026Article
- Capability of chatbots powered by large language models to support the screening process of scoping reviews: a feasibility study.JAMIA open · 2026Article
- Assessing Large Language Models for Early Article Identification in Otolaryngology-Head and Neck Surgery Systematic Reviews.Health care science · 2026Article
- Cybersecurity in Radio Frequency Technologies: A Scientometric and Systematic Review with Implications for IoT and Wireless Applications.Sensors (Basel, Switzerland) · 2026Review
- Human-AI collaboration enhances the performance of large language models in risk of bias assessment.BMC medical research methodology · 2026Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe screening process for systematic reviews and meta-analyses in medical research is a labor-intensive and time-consuming task. While machine learning and deep learning have been applied to facilitate this process, these methods often require training data and user annotation. This study aims to assess the efficacy of ChatGPT, a large language model based on the Generative Pretrained Transformers (GPT) architecture, in automating the screening process for systematic reviews in radiology without the need for training data.
methodsA prospective simulation study was conducted between May 2nd and 24th, 2023, comparing ChatGPT's performance in screening abstracts against that of general physicians (GPs). A total of 1198 abstracts across three subfields of radiology were evaluated. Metrics such as sensitivity, specificity, positive and negative predictive values (PPV and NPV), workload saving, and others were employed. Statistical analyses included the Kappa coefficient for inter-rater agreement, ROC curve plotting, AUC calculation, and bootstrapping for p-values and confidence intervals.
resultsChatGPT completed the screening process within an hour, while GPs took an average of 7-10 days. The AI model achieved a sensitivity of 95% and an NPV of 99%, slightly outperforming the GPs' sensitive consensus (i.e., including records if at least one person includes them). It also exhibited remarkably low false negative counts and high workload savings, ranging from 40 to 83%. However, ChatGPT had lower specificity and PPV compared to human raters. The average Kappa agreement between ChatGPT and other raters was 0.27.
conclusionsChatGPT shows promise in automating the article screening phase of systematic reviews, achieving high sensitivity and workload savings. While not entirely replacing human expertise, it could serve as an efficient first-line screening tool, particularly in reducing the burden on human resources. Further studies are needed to fine-tune its capabilities and validate its utility across different medical subfields.
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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.