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Large Language Model-Assisted Systematic Review: Validation Based on Cochrane Review Data.

Siun Kim1, Hyung-Jin Yoon2,3

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Medicine
  • Evidence-Based Medicine

Background:

  • Systematic reviews are crucial for evidence-based medicine but are time-consuming.
  • Large Language Models (LLMs) present an opportunity to automate parts of this process.

Purpose of the Study:

  • To evaluate the performance of advanced LLMs (GPT-4o, GPT-4o-mini, Llama 3.1:8B) in automating systematic review tasks.
  • To assess LLMs' utility in abstract screening and risk of bias assessment.

Main Methods:

  • LLMs were tested on abstract screening and risk of bias assessment using 12 Cochrane drug intervention reviews.
  • A novel one-shot inclusivity adjustment method was proposed for threshold modulation.

Main Results:

  • GPT-4o demonstrated the highest screening performance (recall 0.894, precision 0.492).
  • Risk of bias assessment accuracy was domain-dependent, with highest accuracy in random sequence generation (0.873) and lowest in selective reporting (0.418).

Conclusions:

  • LLMs show practical utility for automating systematic reviews, particularly in abstract screening.
  • Current LLM applications in systematic reviews have limitations, especially in nuanced risk of bias assessments.