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Using Large Language Models to Address Contextual Questions in Systematic Reviews.

Susanne Hempel1, Kimny Sysawang1, Haley K Holmer2

  • 1Southern California Evidence Review Center University of Southern California Los Angeles California USA.

Cochrane Evidence Synthesis and Methods
|March 2, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can feasibly answer contextual questions in systematic reviews, offering articulate and clinically plausible responses. However, human expertise is crucial for verifying information and ensuring meaningful use of LLM-generated content.

Keywords:
artificial intelligencecontextlarge language modelssystematic reviews

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Area of Science:

  • Health services research
  • Artificial intelligence in healthcare
  • Evidence-based practice

Background:

  • Systematic evidence reviews (SERs) by the Agency for Healthcare Research and Quality (AHRQ) Evidence-based Practice Center (EPC) Program utilize contextual questions for background.
  • A standardized method for addressing these contextual questions in systematic reviews is currently lacking.

Purpose of the Study:

  • To explore the feasibility and validity of using publicly available large language models (LLMs) to address contextual questions in systematic reviews.
  • To compare LLM-generated responses to human-generated responses within SERs.

Main Methods:

  • Selected contextual questions from 25 SERs (20 published, 5 unpublished).
  • Used LLMs (ChatGPT, Bard, Claude, Perplexity) with minimal prompt engineering to generate answers.
  • Two independent reviewers assessed LLM responses against predefined criteria, evaluating feasibility, content/structure validity, errors, bias, and congruence.

Main Results:

  • LLMs demonstrated feasibility in answering contextual questions with articulate, clinically plausible, and well-structured responses.
  • Responses varied in content and format, and were not reproducible due to regular LLM updates.
  • Few factual errors or biases were detected, but citations were often unverifiable ('confabulations').
  • LLM responses provided more background, while SERs offered more nuanced answers; congruence varied.
  • Incremental validity results were mixed, potentially tool-dependent.

Conclusions:

  • Large language models show potential for assisting with contextual questions in systematic reviews.
  • Human expertise remains indispensable for critically evaluating and meaningfully integrating LLM-generated information into systematic reviews.