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Related Experiment Video

Updated: Apr 1, 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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Artificial Intelligence Tools for Automating Evidence Synthesis: Scoping Review.

Sashika Harasgama1, Helen Pearce1, Cameron Appel1

  • 1Wolfson Institute of Population Health, Queen Mary University of London, Whitechapel Campus, London, E1 2AD, United Kingdom, +44 (0)20 7882 5555.

Journal of Medical Internet Research
|March 30, 2026
PubMed
Summary

Artificial intelligence (AI) tools are automating evidence synthesis, with a rise in studies on large language models (LLMs) like ChatGPT. Further research is needed to compare AI approaches and evaluate their real-world impact.

Keywords:
ChatGPTartificial intelligenceautomationevidence synthesislarge language modelsmachine learningsystematic reviews as a topic

Related Experiment Videos

Last Updated: Apr 1, 2026

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

Published on: June 13, 2025

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Area of Science:

  • Information Science
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Evidence synthesis is time-consuming and reviews quickly become outdated.
  • Artificial intelligence (AI) offers automation for literature searching, screening, data extraction, and analysis.
  • Generative AI is increasingly used, necessitating an understanding of its applications and risks in evidence synthesis.

Purpose of the Study:

  • To map the current landscape of evaluated AI tools for automating evidence synthesis.
  • To identify trends in the development and evaluation of AI tools in this field.

Main Methods:

  • A scoping review following Joanna Briggs Institute methodology.
  • Searches of Ovid MEDLINE, Embase, Scopus, Web of Science, and gray literature from January 2021 onward.
  • Independent screening of citations and data extraction on AI tool features.

Main Results:

  • 222 articles identified 65 AI tools and 25 open-source models/algorithms for evidence synthesis automation.
  • A significant trend (54.1% published in 2024) towards researching general-purpose large language models (LLMs), particularly generative pre-trained transformer models like ChatGPT.
  • AI-assisted automation focused on title/abstract screening (61.7%) and data extraction (26.1%), with limited reporting on time/workload outcomes or pragmatic evaluations.

Conclusions:

  • The field is rapidly evolving with a shift towards generative AI for evidence synthesis automation.
  • Significant research gaps exist in comparing different AI approaches and conducting pragmatic evaluations.
  • Careful tool selection, bias mitigation, and integrity checks are crucial for reliable evidence-based decision-making.