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Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56863, first published .
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Development and Validation of a Literature Screening Tool: Few-Shot Learning Approach in Systematic Reviews

Development and Validation of a Literature Screening Tool: Few-Shot Learning Approach in Systematic Reviews

Journals

  1. Gauthier Mongeon J, Ouadfel S, Thullier F, Gaboury S, Arsenault-Lapierre G. Manual versus AI-assisted document screening (ASReview): a comparative analysis within a rapid systematized review in the social sciences. International Journal of Social Research Methodology 2026:1 View
  2. Vivekanantha P, Son H, Bernardini L, Bouchard M, Ayeni O, Kay J. Using a large language model as a third reviewer to augment dual human full‐text screening in orthopaedic systematic reviews. Knee Surgery, Sports Traumatology, Arthroscopy 2026;34(8):3030 View
  3. Liu Y, Yang R, Liew J, Yin Z, Foote H, Lindsell C, Hong C. Leveraging LLMs for Title and Abstract Screening for Systematic Review: A Cost-effective Dynamic Few-shot Learning Approach. Journal of Healthcare Informatics Research 2026;10(3):654 View
  4. Barragán A, Bonett S, Rodríguez‐Grande E, Orjuela‐Cañón A, Perdomo O, Sánchez‐Vanegas G. Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review. Cochrane Evidence Synthesis and Methods 2026;4(5) View
  5. Looareesuwan P, Ponthongmak W, Tansawet A, Ratanatharathorn C, Owasi H, McKay G, Attia J, Oami T, Thakkinstian A, Tariq A. 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 2026;5(8):e0001668 View