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Exploratory Evaluation of Large Language Models for Reducing Language Bias in Systematic Review Screening.

Junki Ikeguchi1, Hiroaki Ueshima2,3, Hiroshi Tamura1,2

  • 1Graduate School of Informatics, Kyoto University, Kyoto, Japan.

Studies in Health Technology and Informatics
|July 3, 2026
PubMed
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Large language models (LLMs) can help systematic reviews overcome language bias. Direct multilingual processing by LLMs maintained high sensitivity for non-English studies, unlike translation methods.

Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Systematic Reviews

Background:

  • Systematic reviews often exclude non-English studies due to resource limitations, introducing language bias.
  • Large language models (LLMs) offer potential solutions for processing multilingual data in research.

Purpose of the Study:

  • To evaluate if direct multilingual processing by LLMs reduces language-based disparities in systematic review abstract screening compared to translation-mediated approaches.

Main Methods:

  • Six state-of-the-art LLMs were tested on English and non-English abstracts.
  • Performance was assessed using direct multilingual screening versus screening translated abstracts.
  • Key metrics included sensitivity, specificity, F1 score, balanced accuracy, and workload reduction.
Keywords:
Large language modelslanguage biasscreening automationsystematic review

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Main Results:

  • All LLMs showed high sensitivity (≥0.938) on English abstracts.
  • Translation-mediated screening significantly decreased sensitivity for some models (0.47-0.54).
  • Direct multilingual LLM processing maintained higher sensitivity (0.71-1.00) for non-English abstracts, with notable model variations.

Conclusions:

  • Direct multilingual LLM screening shows promise in reducing language-related sensitivity disparities in systematic reviews.
  • Further research is needed to understand the impact on downstream meta-analytic bias.