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Related Concept Videos

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Related Experiment Video

Updated: Jan 18, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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ASReview LAB v.2: Open-source text screening with multiple agents and a crowd of experts.

Jonathan de Bruin1, Peter Lombaers2,3, Casper Kaandorp3

  • 1Department of Research and Data Management Services, Information Technology Services, Utrecht University, Utrecht, the Netherlands.

Patterns (New York, N.Y.)
|September 10, 2025
PubMed
Summary

ASReview LAB v.2 enhances AI-assisted systematic reviews with collaborative screening and multiple AI agents. This AI-powered platform significantly improves performance, reducing review time and increasing accuracy for researchers.

Keywords:
active learningcrowdsourcingdata-driven screeninghyperparameter optimizationmachine learningmultiagent systemsopen-source softwarereproducibilitysystematic reviewstransparency

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Area of Science:

  • Artificial Intelligence
  • Bioinformatics
  • Health Informatics

Background:

  • Systematic reviews are crucial for evidence-based practice but are time-consuming.
  • Current AI tools for systematic reviews have limitations in collaboration and model flexibility.
  • ASReview LAB v.1 provided a foundation for AI-assisted screening.

Purpose of the Study:

  • To introduce ASReview LAB v.2, an advanced AI-assisted systematic reviewing platform.
  • To enhance collaborative screening capabilities using a shared AI model and multiple experts.
  • To improve the performance and efficiency of systematic reviews through advanced AI models.

Main Methods:

  • Implemented collaborative screening with a "crowd of oracles" using a shared AI model.
  • Integrated support for multiple AI agents, including general-purpose and domain-specific transformer models.
  • Utilized the SYNERGY benchmark dataset for performance evaluation and model tuning.
  • Focused on user-centric design principles for reproducible and transparent workflows.

Main Results:

  • Achieved a significant 24.1% reduction in loss compared to ASReview LAB v.1.
  • Demonstrated improved performance through model enhancements and hyperparameter tuning.
  • Ensured reproducible and transparent workflows with detailed logging of configuration and annotation data.

Conclusions:

  • ASReview LAB v.2 represents a substantial advancement in AI-assisted systematic reviewing.
  • The platform facilitates efficient, collaborative, and accurate evidence synthesis.
  • Future developments aim to further automate and optimize the systematic review process.