The contradiction between the exponential growth of medical knowledge and the delay in traditional evidence synthesis has necessitated the living evidence (LE) synthesis model, which focuses on continuous updates. The concept of LE synthesis has evolved into a dynamic full-lifecycle spectrum ranging from evidence maps and systematic reviews to clinical guidelines. Although the framework by Bendersky et al. established a methodological baseline for regulating this complex system, its reliance on human labor and a "process-driven" logic creates significant efficiency bottlenecks when dealing with massive heterogeneous data. In the context of the technological revolution driven by AI and large language models (LLMs), this paper analyzes the limitations of current frameworks and demonstrates the necessity of evolving from "mechanical process execution" to a paradigm of "human-machine collaborative cognitive augmentation". This shift demands a restructuring of how evidence is discovered and integrated. Furthermore, by combining China's advantages in clinical data resources with current standardization efforts, we explore localized pathways that integrate technological foresight with evidence-based rigor. This aims to build a smart, dynamic evidence ecosystem dedicated to high-quality health decision-making.