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Related Experiment Video

Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Machine-learning assisted screening for evidence synthesis: Methodological case study of the ASReview tool.

Kim Boesen1, Pascal Dueblin2, Lars G Hemkens1

  • 1Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University of Basel and University Hospital Basel, Basel, Switzerland.

Journal of Clinical and Translational Science
|December 15, 2025
PubMed
Summary
This summary is machine-generated.

ASReview software streamlines literature screening for systematic reviews by ranking records. This tool efficiently identifies relevant studies, potentially saving resources compared to traditional methods.

Keywords:
ASreviewEvidence synthesisartificial intelligencereview softwarestudy selection

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Area of Science:

  • Medical Informatics
  • Systematic Review Methodology
  • Cancer Research

Background:

  • Systematic literature reviews are crucial for evidence-based medicine but are labor-intensive.
  • Automating or semi-automating literature screening can significantly reduce workload and time.
  • Cancer immunotherapy research requires efficient methods for synthesizing trial data.

Purpose of the Study:

  • To evaluate the feasibility, advantages, and limitations of ASReview software for literature screening.
  • To populate a database of cancer immunotherapy trials using ASReview.
  • To assess ASReview's potential to reduce workload and save resources in systematic reviews.

Main Methods:

  • ASReview software was used to rank retrieved records for literature screening.
  • The tool's usability, efficiency, and effectiveness in identifying relevant records were assessed.
  • A database of cancer immunotherapy trials was populated using the software.

Main Results:

  • ASReview demonstrated ease of use and efficiency in identifying relevant records.
  • The software has the potential to save resources compared to traditional two-human-reviewer systematic reviews.
  • Predefined procedures are essential for maintaining transparency and reproducibility.

Conclusions:

  • ASReview is a feasible tool for reducing literature screening workload in systematic reviews.
  • The software offers advantages in efficiency but requires careful implementation for transparency and reproducibility.
  • Limitations include difficulties in adding references to existing projects and the algorithm's learning behavior.