ArticleJournal of the American Medical Informatics Association : JAMIA2025
High-performance automated abstract screening with large language model ensembles.
Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech-touch integration for affective human-robot interaction: a scoping review.Frontiers in robotics and AI · 2026Pooled it
- Batch Size Effects on Mid-2025 State-of-the-Art Large Language Model Performance in Automated Title and Abstract Screening.Cochrane evidence synthesis and methods · 2026Article
- A Dataset for Evaluating Large Language Models on Chinese National Medical Licensing Examinations.Scientific data · 2026Article
- Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review.Journal of medical Internet research · 2026Article
- Compact large language models for title and abstract screening in systematic reviews: An assessment of feasibility, accuracy, and workload reduction.Research synthesis methods · 2026Article
- Large language model-based multiagent collaboration for abstract screening toward automated systematic reviews.Biology methods & protocols · 2026Article
- From Research to Practice in Days, not Decades: Why Leaders Must Act now.Journal of medical systems · 2025Article
- A foundation model for human-AI collaboration in medical literature mining.Nature communications · 2025Article
- Automated analyses of risk of bias and critical appraisal of systematic reviews (ROBIS and AMSTAR 2): a comparison of the performance of 4 large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Treatment allocation in ophthalmological randomised-control trials (TAO-RCT): A cross-sectional meta-research study.Eye (London, England) · 2025Article
- Review
- Accelerating clinical evidence synthesis with large language models.NPJ digital medicine · 2025Article
- Article
- Accelerating Disease Model Parameter Extraction: An LLM-Based Ranking Approach to Select Initial Studies For Literature Review Automation.Machine learning and knowledge extraction · 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
10 authors.
Funding
Abstract
objectivescreening is a labor-intensive component of systematic review involving repetitive application of inclusion and exclusion criteria on a large volume of studies. We aimed to validate large language models (LLMs) used to automate abstract screening. MATERIALS AND
methodsLLMs (GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o, Llama 3 70B, Gemini 1.5 Pro, and Claude Sonnet 3.5) were trialed across 23 Cochrane Library systematic reviews to evaluate their accuracy in zero-shot binary classification for abstract screening. Initial evaluation on a balanced development dataset (n = 800) identified optimal prompting strategies, and the best performing LLM-prompt combinations were then validated on a comprehensive dataset of replicated search results (n = 119 695).
resultsOn the development dataset, LLMs exhibited superior performance to human researchers in terms of sensitivity (LLMmax = 1.000, humanmax = 0.775), precision (LLMmax = 0.927, humanmax = 0.911), and balanced accuracy (LLMmax = 0.904, humanmax = 0.865). When evaluated on the comprehensive dataset, the best performing LLM-prompt combinations exhibited consistent sensitivity (range 0.756-1.000) but diminished precision (range 0.004-0.096) due to class imbalance. In addition, 66 LLM-human and LLM-LLM ensembles exhibited perfect sensitivity with a maximal precision of 0.458 with the development dataset, decreasing to 0.1450 over the comprehensive dataset; but conferring workload reductions ranging between 37.55% and 99.11%. DISCUSSION: Automated abstract screening can reduce the screening workload in systematic review while maintaining quality. Performance variation between reviews highlights the importance of domain-specific validation before autonomous deployment. LLM-human ensembles can achieve similar benefits while maintaining human oversight over all records.
conclusionLLMs may reduce the human labor cost of systematic review with maintained or improved accuracy, thereby increasing the efficiency and quality of evidence synthesis.
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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.