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

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Accelerating the pace and accuracy of systematic reviews using AI: a validation study.

Jiada Zhan1, Kara Suvada2, Muwu Xu3

  • 1Nutrition and Health Sciences, Laney Graduate School, Emory University, Atlanta, GA, USA. jzha832@emory.edu.

Systematic Reviews
|December 19, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) significantly improves efficiency in systematic reviews, screening titles/abstracts and full-text articles faster than humans. While AI shows high accuracy in initial screening, its performance in full-text review requires careful consideration.

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

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

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Systematic Review Methodology

Background:

  • Artificial intelligence (AI) offers potential efficiency gains in systematic literature reviews and meta-analyses.
  • The accuracy of AI tools in screening titles/abstracts and full-text articles remains uncertain.

Purpose of the Study:

  • To evaluate the performance metrics of a GPT-4 AI program, Review Copilot.
  • To compare AI performance against human decisions (gold standard) in screening systematic review articles.

Main Methods:

  • Utilized participant data from four published systematic reviews/meta-analyses.
  • Compared Review Copilot's sensitivity and specificity against human screening of titles/abstracts and full-text articles.
  • Analyzed screening time, agreement between runs, and kappa statistics.

Main Results:

  • Review Copilot achieved 99.2% sensitivity and 83.6% specificity for title/abstract screening.
  • Full-text screening sensitivity was 97.6%, with 47.4% specificity.
  • AI screening was four times faster than human screening, with 95.4% agreement between runs.

Conclusions:

  • AI is increasingly integral to systematic reviews and meta-analyses.
  • Understanding AI's capabilities and limitations is crucial for ethical research and evidence-based healthcare decisions.