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Automating the data extraction process for systematic reviews using GPT-4o and o3.

Yuki Kataoka1,2,3,4, Tomohiro Takayama5,6, Keisuke Yoshimura5

  • 1Department of Internal Medicine, Kyoto Min-iren Asukai Hospital, Kyoto, Japan.

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Summary
This summary is machine-generated.

We developed an automated data extraction system using GPT-4o for systematic reviews. While effective for string data, it requires human oversight for numeric data extraction.

Keywords:
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Area of Science:

  • Artificial Intelligence
  • Biomedical Informatics
  • Health Sciences

Background:

  • Automating data extraction (DE) in systematic reviews (SRs) is crucial for efficiency.
  • Existing automated DE methods often require significant manual input.
  • Large language models (LLMs) show potential for improving DE automation.

Purpose of the Study:

  • To develop and evaluate an open-source, fully automated DE system using GPT-4o for SRs.
  • To assess the system's performance on diverse datasets, including string and numeric variables.
  • To compare the automated system's accuracy with manual extraction.

Main Methods:

  • Developed an open-source DE system leveraging GPT-4o with no human intervention during extraction.
  • Trained and optimized the system on a dataset of 290 randomized controlled trials (RCTs).
  • Validated the system on two independent datasets, including an updated search and a separate published study.

Main Results:

  • The system achieved high variable detection comprehensiveness (93.5%) but variable accuracy differed between string (higher) and numeric (lower) data.
  • Initial external validation showed a mean accuracy of 84.4% for GPT-4o.
  • Adjusted DE methods improved accuracy to 96.3% on the external dataset, comparable to manual extraction.

Conclusions:

  • The GPT-4o-based system shows promise for assisting with string variable DE in SRs.
  • The system currently cannot fully replace human reviewers for numeric data extraction.
  • Further research is needed to establish the broader applicability of this automated DE system across various review contexts.