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Updated: Oct 9, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

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Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses.

Candyce Hamel1, Mona Hersi2, Shannon E Kelly3,4

  • 1Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada. cahamel@ohri.ca.

BMC Medical Research Methodology
|December 21, 2021
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Summary
This summary is machine-generated.

Artificial intelligence and active-machine learning (AML) can accelerate systematic reviews. This study provides a seven-step framework to help research teams integrate AML tools for efficient title and abstract screening.

Keywords:
Active machine-learningArtificial intelligenceBest practice guidanceKnowledge SynthesisPrioritizationTitle and abstract screening

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Area of Science:

  • Evidence-based medicine
  • Systematic reviews
  • Knowledge synthesis

Background:

  • Systematic reviews are crucial for evidence-based medicine but are time-consuming.
  • There's a growing need for faster evidence production while maintaining methodological rigor.
  • Artificial intelligence (AI) and active-machine learning (AML) are increasingly used in systematic review software.

Purpose of the Study:

  • To provide practical recommendations for integrating AI and AML into systematic review workflows.
  • To address challenges in setting up and utilizing AI/AML tools for title and abstract screening.
  • To offer guidance for knowledge synthesis teams adopting these technologies.

Main Methods:

  • Retrospective evaluation of AML implementation in ten completed systematic reviews.
  • Analysis of barriers and facilitators encountered during prospective AML tool use.
  • Development of a seven-step framework for integrating AI and AML in screening processes.

Main Results:

  • A seven-step framework is presented for integrating AI and AML into title and abstract screening.
  • Steps include team preparation, database management, training set development, and ongoing screening.
  • The framework allows for team optimization by reallocating members to other review stages.

Conclusions:

  • AI and AML are effective tools for title and abstract screening in systematic reviews.
  • Successful integration requires careful consideration of workflow and team dynamics.
  • Transparent reporting of AI/AML methods is essential for future research and evaluation.