The Reporting and Data Systems (RADS), established by the American College of Radiology (ACR), serve as structured risk-stratification frameworks designed to mitigate subjectivity and enhance consistency in radiological interpretation. Despite their widespread adoption, RADS still face methodological challenges in real-world practice, including limited inter-observer consistency, insufficient feature quantification, and suboptimal cross-center reproducibility. In recent years, the evolution of Artificial Intelligence (AI), particularly Large Language Models (LLMs), has offered novel pathways to address these limitations. This paper systematically reviews the structural characteristics and practical constraints of major RADS frameworks and synthesizes empirical progress regarding AI in lesion identification, risk re-stratification, and workflow standardization. Current evidence suggests that AI is best positioned as an "augmentative layer" for RADS to bolster objectivity and reproducibility. Furthermore, this article explores the potential of LLMs in semantic understanding, automated reasoning, and quality control, while projecting the evolution of human-computer collaboration from a regulatory perspective. This trend holds significant implications for promoting the homogenization of diagnostic imaging, empowering primary healthcare services, and establishing intelligent regulatory systems in China.