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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Data Extractions Using a Large Language Model (Elicit) and Human Reviewers in Randomized Controlled Trials: A

Joleen Bianchi1,2, Julian Hirt1,3,4, Magdalena Vogt1

  • 1Department of Health Eastern Switzerland University of Applied Sciences St. Gallen Switzerland.

Cochrane Evidence Synthesis and Methods
|September 29, 2025
PubMed
Summary
This summary is machine-generated.

Elicit, an AI tool, partially extracts data for systematic reviews but requires human verification for accuracy. Human reviewers are essential to ensure complete and correct data extraction from randomized controlled trials.

Keywords:
artificial intelligencedata extractionhuman reviewerrandomized controlled trialsystematic review

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Area of Science:

  • Medical research methodology
  • Artificial intelligence in healthcare

Background:

  • Systematic reviews are crucial for evidence-based medicine but are labor-intensive.
  • Artificial intelligence (AI) tools like Elicit may streamline systematic review processes, particularly data extraction.
  • The accuracy and performance of Elicit for data extraction have not been independently validated.

Purpose of the Study:

  • To compare the accuracy of data extraction from randomized controlled trials (RCTs) using the AI tool Elicit versus human reviewers.
  • To assess Elicit's performance across various data variables within RCTs.

Main Methods:

  • A comparative analysis was conducted on 20 RCTs from diverse healthcare topics and 11 countries.
  • Data extraction was performed by both Elicit and human reviewers for predefined variables: study objectives, sample characteristics/size, study design, interventions, outcomes, and intervention effects.
  • Extracted data were classified as "more," "equal to," "partially equal," or "deviating" compared to human extraction, using the STROBE checklist for reporting.

Main Results:

  • Elicit demonstrated partial accuracy, with "more" data extracted in 29.3%, "equal" in 20.7%, "partially equal" in 45.7%, and "deviating" in 4.3% of cases across seven variables.
  • Elicit excelled in extracting study design (100% "more") and sample characteristics (45% "more").
  • For complex variables like "intervention effects" and "interventions," Elicit's extractions were less detailed, with 95% rated as "partially equal."

Conclusions:

  • Elicit can partially extract data for systematic reviews but requires human oversight for nuanced variables.
  • Human reviewers remain essential for ensuring the completeness and accuracy of data extraction, especially for intervention details and effects.
  • While AI tools can facilitate data extraction, verification by human reviewers is necessary for reliable systematic reviews.