ArticleJournal of medical Internet research2024
Automated Paper Screening for Clinical Reviews Using Large Language Models: Data Analysis Study.
Article in Journal of medical Internet research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 98 papers, 8 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
98 citing papers in PubMed, 8 syntheses or guidelines pooled it, 154 citations in OpenAlex.
- Human versus artificial intelligence: evaluating ChatGPT's performance in conducting published systematic reviews with meta-analysis in chronic pain research.Regional anesthesia and pain medicine · 2026Pooled it
- Tools for dementia disclosure: a systematic review of -family caregivers' perspectives and experiences.The Gerontologist · 2026Pooled it
- Evidence integration and bridging methodologies for high-risk and innovative medical devices andFrontiers in medicine · 2026Pooled it
- How Well Do ChatGPT and Claude Perform in Study Selection for Systematic Review in Obstetrics.Journal of medical systems · 2025Pooled 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
- Can AI assess literature like experts? An entropy-based comparison of ChatGPT-4o, DeepSeek R1, and human ratings.Frontiers in research metrics and analytics · 2025Pooled it
- Research priorities in chronic lung allograft dysfunction: A machine learning-assisted bibliometric analysis.JHLT open · 2026Article
- Integration of large language models and evidence-based Chinese medicine: A scoping review.Integrative medicine research · 2026Review
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Transforming Systematic Reviews: Evaluating a Fine-Tuned Large Language Model for Abstract Screening in Uveitis and Retinal Vasculitis: Fine-Tuned LLM for Review Screening.Ophthalmology science · 2026Article
- Toward Automating the Selection of Articles Reporting EQ-5D Data for Systematic Literature Reviews Using Large Language Models: Algorithm Development and Evaluation Study.JMIR formative research · 2026Article
- Performance of Two AI Approaches in ASReview Compared With Manual Screening for Dementia Care Literature Screening: Comparative Analysis.JMIR formative research · 2026Article
- Article
- Can artificial intelligence accurately assess systematic review quality? Benchmarking large language models for AMSTAR 2 appraisal in dental evidence synthesis.Evidence-based dentistry · 2026Article
- Stepwise Diagnostic Evaluation of Chinese Large Language Models: Comparative Study of Common and Rare Diseases.Journal of medical Internet research · 2026Article
- 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
- Automated data extraction for systematic reviews using GPT-5.2 and Google Gemini Pro 3: A dual-large language model approach in orthopaedic research.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- Article
38 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe systematic review of clinical research papers is a labor-intensive and time-consuming process that often involves the screening of thousands of titles and abstracts. The accuracy and efficiency of this process are critical for the quality of the review and subsequent health care decisions. Traditional methods rely heavily on human reviewers, often requiring a significant investment of time and resources.
objectiveThis study aims to assess the performance of the OpenAI generative pretrained transformer (GPT) and GPT-4 application programming interfaces (APIs) in accurately and efficiently identifying relevant titles and abstracts from real-world clinical review data sets and comparing their performance against ground truth labeling by 2 independent human reviewers.
methodsWe introduce a novel workflow using the Chat GPT and GPT-4 APIs for screening titles and abstracts in clinical reviews. A Python script was created to make calls to the API with the screening criteria in natural language and a corpus of title and abstract data sets filtered by a minimum of 2 human reviewers. We compared the performance of our model against human-reviewed papers across 6 review papers, screening over 24,000 titles and abstracts.
resultsOur results show an accuracy of 0.91, a macro F
conclusionsLarge language models have the potential to streamline the clinical review process, save valuable time and effort for researchers, and contribute to the overall quality of clinical reviews. By prioritizing the workflow and acting as an aid rather than a replacement for researchers and reviewers, models such as GPT-4 can enhance efficiency and lead to more accurate and reliable conclusions in medical research.
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.