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Related Experiment Video

Updated: Jan 12, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Assessing the Feasibility and Acceptability of a Bespoke Large Language Model Pipeline to Extract Data From Different

Zalaya Simmons1,2, Beti Evans1, Tamsyn Harris3

  • 1Research, Evidence and Knowledge Division, Chief Scientific Officer Group UK Health Security Agency (UKHSA) London UK.

Cochrane Evidence Synthesis and Methods
|November 6, 2025
PubMed
Summary

Large language models (LLMs) show promise for automating data extraction across diverse study designs, achieving 68% acceptability with human oversight. Further validation is needed before integration into review workflows.

Keywords:
artificial intelligencedata extractionevidence synthesisfeasibilitylarge language modelpublic healthsystematic review

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Area of Science:

  • Artificial Intelligence
  • Medical Informatics
  • Data Science

Background:

  • Data extraction is a crucial, time-consuming component of evidence reviews.
  • Existing research highlights the efficiency gains of artificial intelligence (AI) and large language models (LLMs) for randomized controlled trials.
  • The efficacy of LLMs for data extraction across diverse study designs remains largely unexplored.

Purpose of the Study:

  • To assess the performance of a bespoke LLM pipeline (Retrieval-Augmented Generation with LLaMa 3-70B) for automating data extraction.
  • To evaluate the accuracy and reliability of LLM-driven data extraction across various study designs.
  • To determine the acceptability of LLM outputs for real-world evidence review applications.

Main Methods:

  • A Retrieval-Augmented Generation pipeline utilizing LLaMa 3-70B was developed for automated data extraction.
  • Accuracy was evaluated by comparing LLM extractions against human extractions for 173 data fields from 24 articles.
  • Reliability was assessed using the mean maximum agreement rate across 116 data fields from 16 studies.

Main Results:

  • 68% of the 173 evaluated data fields were rated as acceptable by human reviewers.
  • Acceptability varied by data field, with high scores for "objective," "setting," and "study design" (≥90%), but lower scores for "outcome" and "time period" (≤54%).
  • The mean maximum agreement rate for reliability was 0.71 (SD: 0.28), indicating variability across data fields.

Conclusions:

  • LLMs, when combined with human quality assurance, can potentially support data extraction in evidence reviews encompassing multiple study designs.
  • Further performance enhancements and validation are necessary for the successful integration of LLM-based tools into systematic review workflows.