The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provides a rigorous foundation for evidence synthesis, yet the manual processes of data extraction and literature screening remain time-consuming and restrictive. Recent advances in Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), offer opportunities to automate and scale these tasks, thereby improving time and efficiency. However, reproducibility, transparency, and auditability, the core PRISMA principles, are being challenged by the inherent non-determinism of LLMs and the risks of hallucination and bias amplification. To address these limitations, this study integrates human-led synthesis with a GenAI-assisted statistical pre-screening step. Human oversight ensures scientific validity and transparency, while the deterministic nature of the statistical layer enhances reproducibility. The proposed approach systematically enhances PRISMA guidelines, providing a responsible pathway for incorporating GenAI into systematic review workflows.

翻译：系统综述和荟萃分析的首选报告项目（PRISMA）框架为证据综合提供了严谨的基础，但数据提取和文献筛选的手动流程仍然耗时且受限。生成式人工智能（GenAI）的最新进展，特别是大型语言模型（LLMs），为自动化和规模化这些任务提供了机遇，从而提升了时效性和效率。然而，重复性、透明性和可审计性——这些PRISMA的核心原则——正受到LLMs固有的非确定性与幻觉及偏差放大风险的挑战。为解决这些局限性，本研究将人类主导的综合分析与GenAI辅助的统计预筛选步骤相结合。人类监督确保科学有效性和透明性，而统计层的确定性特性增强了可重复性。所提出的方法系统性地增强了PRISMA指南，为将GenAI纳入系统综述工作流提供了一条负责任的路径。