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This paper is in the following e-collection/theme issue:

Artificial Intelligence (4549) Digital Health Reviews (3524) Natural Language Processing (1228) Machine Learning (3058) Applications of AI (864)

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

Authors of this article:

Phongphat Wiwatthanasetthakarn1 Author Orcid Image ;   Wanchana Ponthongmak1 Author Orcid Image ;   Panu Looareesuwan1 Author Orcid Image ;   Amarit Tansawet2 Author Orcid Image ;   Pawin Numthavaj1 Author Orcid Image ;   Gareth J McKay3 Author Orcid Image ;   John Attia4 Author Orcid Image ;   Ammarin Thakkinstian1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

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 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 View