ArticleJournal of the American Medical Informatics Association : JAMIA2024
A question-answering framework for automated abstract screening using large language models.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
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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
13 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Advancing Evidence-Based Medicine for Population, Intervention, Comparison, and Outcome Element Recognition and Extraction in Medical Literature: Large Language Model Approach.Journal of medical Internet research · 2026Article
- To include or not to include? A prescription from the pharmacy on how to use active learning-assisted screening in systematic reviews.Systematic reviews · 2026Article
- Comparing five generative AI chatbots' answers to LLM-generated clinical questions with medical information scientists' evidence summaries.Journal of the Medical Library Association : JMLA · 2026Article
- Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review.Journal of medical Internet research · 2026Article
- Comparison of three large language models' ability to assess the risk of bias using ROBINS-I tool.BMJ digital health & AI · 2026Article
- Large language model-based multiagent collaboration for abstract screening toward automated systematic reviews.Biology methods & protocols · 2026Article
- Comparing Five Generative AI Chatbots' Answers to LLM-Generated Clinical Questions with Medical Information Scientists' Evidence Summaries.medRxiv : the preprint server for health sciences · 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
- Analysis of article screening and data extraction performance by an AI systematic literature review platform.Frontiers in artificial intelligence · 2025Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Title and abstract screening for literature reviews using large language models: an exploratory study in the biomedical domain.Systematic reviews · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
objectiveThis paper aims to address the challenges in abstract screening within systematic reviews (SR) by leveraging the zero-shot capabilities of large language models (LLMs).
methodsWe employ LLM to prioritize candidate studies by aligning abstracts with the selection criteria outlined in an SR protocol. Abstract screening was transformed into a novel question-answering (QA) framework, treating each selection criterion as a question addressed by LLM. The framework involves breaking down the selection criteria into multiple questions, properly prompting LLM to answer each question, scoring and re-ranking each answer, and combining the responses to make nuanced inclusion or exclusion decisions. RESULTS AND DISCUSSION: Large-scale validation was performed on the benchmark of CLEF eHealth 2019 Task 2: Technology-Assisted Reviews in Empirical Medicine. Focusing on GPT-3.5 as a case study, the proposed QA framework consistently exhibited a clear advantage over traditional information retrieval approaches and bespoke BERT-family models that were fine-tuned for prioritizing candidate studies (ie, from the BERT to PubMedBERT) across 31 datasets of 4 categories of SRs, underscoring their high potential in facilitating abstract screening. The experiments also showcased the viability of using selection criteria as a query for reference prioritization. The experiments also showcased the viability of the framework using different LLMs.
conclusionInvestigation justified the indispensable value of leveraging selection criteria to improve the performance of automated abstract screening. LLMs demonstrated proficiency in prioritizing candidate studies for abstract screening using the proposed QA framework. Significant performance improvements were obtained by re-ranking answers using the semantic alignment between abstracts and selection criteria. This further highlighted the pertinence of utilizing selection criteria to enhance abstract screening.
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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.