Can large language models replace humans in systematic reviews? Evaluating GPT ‐4's efficacy in screening and extracting data from peer‐reviewed and grey literature in multiple languages

AbstractSystematic reviews are vital for guiding practice, research and policy, although they are often slow and labour-intensive. Large language models (LLMs) could speed up and automate systematic reviews, but their performance in such tasks has yet to be comprehensively evaluated against humans, and no study has tested Generative Pre-Trained Transformer (GPT)-4, the biggest LLM so far. This pre-registered study uses a “human-out-of-the-loop” approach to evaluate GPT-4's capability in title/abstract screening, full-text review and data extraction across various literature types and languages. Although GPT-4 had accuracy on par with human performance in some tasks, results were skewed by chance agreement and da taset imbalance. Adjusting for these caused performance scores to drop across all stages: for data extraction, performance was moderate, and for screening, it ranged from none in highly balanced literature datasets (~1:1) to moderate in those datasets where the ratio of inclusion to exclusion in stu dies was imbalanced (~1:3). When screening full-text literature using highly reliable prompts, GPT-4's performance was more robust, reaching “human-like” levels. Although our findings indicate that, currently, substantial caution should be exercised if LLMs are being used to conduct systematic r eviews, they also offer preliminary evidence that, for certain review tasks delivered under specific conditions, LLMs can rival human performance.

Source: Research Synthesis Methods - March 15, 2024 Category: Chemistry Authors: Qusai Khraisha, Sophie Put, Johanna Kappenberg, Azza Warraitch, Kristin Hadfield Tags: RESEARCH ARTICLE Source Type: research