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Related Experiment Video

Updated: Jan 29, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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The Phases of Living Evidence Synthesis Using AI AI: Living Evidence Synthesis (Version 1).

Xuping Song1,2,3,4, Zhenjie Lian1,2, Rui Wang2,5

  • 1School of Public Health, Lanzhou University, No. 222 South Tianshui Road, Lanzhou, Lanzhou, Gansu, 753000, China, +86 13893117077, +86 13893117077.

Journal of Medical Internet Research
|January 27, 2026
PubMed
Summary

Artificial intelligence (AI) and semiautomated tools enhance living evidence (LE) synthesis by improving efficiency and accuracy in data extraction and risk assessment. These tools show high recall and F1-scores, optimizing the LE synthesis process.

Keywords:
accuracyartificial intelligenceefficiencyliving evidence synthesisphasessemiautomated toolsutility

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Area of Science:

  • Evidence synthesis
  • Health informatics
  • Artificial intelligence

Background:

  • Living evidence (LE) synthesis continuously updates systematic reviews to incorporate new findings.
  • The COVID-19 pandemic accelerated LE synthesis, increasing AI applications.
  • The optimal phases for AI integration in LE synthesis remain unclear.

Purpose of the Study:

  • To identify phases within LE synthesis where AI is utilized.
  • To evaluate AI's impact on the efficiency, accuracy, and utility of LE synthesis.

Main Methods:

  • Searched multiple databases (e.g., PubMed, Web of Science) for studies on AI in LE synthesis.
  • Included studies using or developing AI/semiautomated tools in LE synthesis phases.
  • Utilized Covidence for monthly screening and extraction to support LE synthesis.

Main Results:

  • 24 studies included: 17 on LE synthesis (4 with tool development) and 7 on living meta-analyses (3 with tool development).
  • 34 AI/semiautomated tools identified; machine learning classifiers and Living Interactive Evidence synthesis platform were most frequent.
  • AI/semiautomated tools predominantly used for data extraction/collection and risk of bias assessment (20 tools); only 1 tool for publication updates.
  • Studies showed improved efficiency (time, workload, conflict rate) and high accuracy (mean recall 96.24%, mean F1-score 92.17%).

Conclusions:

  • AI and semiautomated tools primarily support data extraction, collection, and risk of bias assessment in LE synthesis.
  • These tools enhance LE synthesis efficiency, demonstrating high accuracy, recall, and F1-scores, though precision varies.