  Systematic reviews are vital for guiding practice, research, and policy, yet
they are often slow and labour-intensive. Large language models (LLMs) could
offer a way to speed up and automate systematic reviews, but their performance
in such tasks has not been comprehensively evaluated against humans, and no
study has tested GPT-4, the biggest LLM so far. This pre-registered study
evaluates GPT-4's capability in title/abstract screening, full-text review, and
data extraction across various literature types and languages using a
'human-out-of-the-loop' approach. Although GPT-4 had accuracy on par with human
performance in most tasks, results were skewed by chance agreement and dataset
imbalance. After adjusting for these, there was a moderate level of performance
for data extraction, and - barring studies that used highly reliable prompts -
screening performance levelled at none to moderate for different stages and
languages. When screening full-text literature using highly reliable prompts,
GPT-4's performance was 'almost perfect.' Penalising GPT-4 for missing key
studies using highly reliable prompts improved its performance even more. Our
findings indicate that, currently, substantial caution should be used if LLMs
are being used to conduct systematic reviews, but suggest that, for certain
systematic review tasks delivered under reliable prompts, LLMs can rival human
performance.
