用大模型自动筛文献，效率提升95%且零漏判。

Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We propose and evaluate an in-house system based on Large Language Models (LLMs) for automating both title/abstract and full-text screening, addressing a critical gap in the literature. Using a completed SR on Vitamin D and falls (14,439 articles), the LLM-based system employed prompt engineering for title/abstract screening and Retrieval-Augmented Generation (RAG) for full-text screening. The system achieved an article exclusion rate (AER) of 99.5%, specificity of 99.6%, a false negative rate (FNR) of 0%, and a negative predictive value (NPV) of 100%. After screening, only 78 articles required manual review, including all 20 identified by traditional methods, reducing manual screening time by 95.5%. For comparison, Rayyan, a commercial tool for title/abstract screening, achieved an AER of 72.1% and FNR of 5% when including articles Rayyan considered as undecided or likely to include. Lowering Rayyan's inclusion thresholds improved FNR to 0% but increased screening time. By addressing both screening phases, the LLM-based system significantly outperformed Rayyan and traditional methods, reducing total screening time to 25.5 hours while maintaining high accuracy. These findings highlight the transformative potential of LLMs in SR workflows by offering a scalable, efficient, and accurate solution, particularly for the full-text screening phase, which has lacked automation tools.