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Artificial Intelligence-Assisted Data Extraction With a Large Language Model: A Study Within Reviews.

Gerald Gartlehner1, Shannon Kugley2, Karen Crotty2

  • 1Social, Statistical, and Environmental Sciences, RTI International, Research Triangle Park, North Carolina, and Department for Evidence-based Medicine and Evaluation, University for Continuing Education Krems, Krems, Austria (G.G.).

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Summary
This summary is machine-generated.

Large language models (LLMs) offer a more efficient alternative for data extraction in evidence synthesis, reducing time and improving accuracy compared to human-only methods. This AI-assisted approach shows promise for streamlining systematic reviews.

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Area of Science:

  • Health Services Research
  • Artificial Intelligence in Healthcare
  • Systematic Review Methodology

Background:

  • Data extraction is crucial for evidence synthesis but is often time-consuming and prone to errors.
  • Large Language Models (LLMs) present a novel AI solution for data extraction, notably without requiring pre-labeled training data.

Purpose of the Study:

  • To compare the efficiency and accuracy of AI-assisted data extraction using LLMs against traditional human-only methods.
  • To evaluate the performance of LLM-based data extraction within real-world systematic review workflows.

Main Methods:

  • A prospective, parallel-group comparison (Study Within A Review - SWAR) was conducted across 6 ongoing systematic reviews.
  • LLMs (Claude versions 2.1, 3.0 Opus, 3.5 Sonnet) performed initial data extraction, followed by human reviewer verification.
  • Key metrics included concordance, time on task, accuracy, sensitivity, positive predictive value, and error analysis compared to a reference standard.

Main Results:

  • The AI-assisted approach demonstrated higher accuracy (91.0%) than human-only extraction (89.0%) and reduced extraction time by a median of 41 minutes per study.
  • Concordance between methods was 77.2%, with AI-assisted extraction showing higher sensitivity (89.4%) and positive predictive value (99.2%).
  • Missed data items were the most frequent error type for both methods, with AI-assisted extraction having slightly lower error rates (9.0% vs. 11.0%).

Conclusions:

  • AI-assisted data extraction using LLMs provides a viable and more efficient alternative to traditional human-only methods in evidence synthesis.
  • The findings suggest that LLMs can significantly streamline the systematic review process, potentially improving resource allocation and review speed.