Transforming literature screening: The emerging role of large language models in systematic reviews

Systematic reviews (SR) synthesize evidence-based medical literature, but they involve labor-intensive manual article screening. Large language models (LLMs) can select relevant literature, but their quality and efficacy are still being determined compared to humans. We evaluated the overlap between title- and abstract-based selected articles of 18 different LLMs and human-selected articles for three SR. In the three SRs, 185/4,662, 122/1,741, and 45/66 articles have been selected and considered for full-text screening by two independent reviewers. Due to technical variations and the inability of the LLMs to classify all records, the LLM's considered sample sizes were smaller. However, on average, the 18 LLMs…

• IN
  Institut National de la Santé et de la Recherche Médicale
• UM
  Universiteit Maastricht
• UH
  Universität Hamburg
• SU
  Syddansk Universitet
• ZU
  Zagazig University
• SU
  Sorbonne Université
• UH
  Universitätsklinikum Hamburg-Eppendorf