The Use of Generative Artificial Intelligence in Systematic Literature Reviews: A Rapid Review of the Literature

Systematic literature reviews (SLRs) underpin life sciences research but are resource intensive. Generative artificial intelligence (GenAI), particularly large language models, may accelerate key SLR tasks; yet, performance and reliability for evidence synthesis remain unclear. This manuscript aims to review current evidence on GenAI performance across core SLR tasks.

We conducted a PRISMA-adapted rapid evidence assessment of English-language biomedical studies published from November 2022 to July 2025 evaluating GenAI or large language models for SLR tasks, including search strategy development, title/abstract screening, full-text screening, data extraction, risk-of-bias assessment, qualitative synthesis, report writing, and end-to-end review generation. Findings were summarized qualitatively by task.

Among 115 included studies, evidence supporting the use of GenAI was strongest for title/abstract screening (n = 51) and data extraction (n = 33). Selected high-quality evaluations reported sensitivities ≥ 90%, workload reductions of 27% to 71%, and human-comparable or superior performance in calibrated human-in-the-loop workflows. Evidence for full-text screening (n = 15) and risk-of-bias assessment (n = 17) was more variable, showing gains in structured or fine-tuned implementations but persistent limitations in specificity and nuanced judgment. For search strategy development, qualitative synthesis, and report writing, GenAI was most effective as a supportive tool; fully autonomous end-to-end SLR generation was unreliable.

GenAI can improve efficiency across multiple SLR tasks when used in hybrid human-AI workflows. Current evidence supports targeted, task-specific adoption with transparent reporting and human oversight, rather than full automation.