The Use of Generative Artificial Intelligence in Systematic Literature Reviews: A Rapid Review of the Literature.
OBJECTIVES Systematic literature reviews (SLRs) underpin life sciences research but are resource intensive. Generative artificial intelligence, particularly large language models (LLMs), 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. METHODS We conducted a PRISMA-adapted rapid evidence assessment of English-language biomedical studies published from November 2022 to July 2025 evaluating GenAI or LLMs for systematic literature review 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. RESULTS 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 27-71%, 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. CONCLUSIONS 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.