Trends in AI-based diagnosis and intervention of metabolic diseases: a bibliometric analysis of the literature from 2000 to 2024

This study provides a comprehensive bibliometric overview of the evolution and research trends of AI in the diagnosis and intervention of metabolic diseases. The analysis highlights three major research frontiers: AI-assisted prevention using smart devices, multimodal diagnostic approaches, and intervention strategies guided by large language models (LLMs). Overall, the findings offer valuable insights into the ongoing transformation of metabolic disease management through AI technologies and lay the groundwork for future research aimed at advancing intelligent diagnostic and therapeutic systems.