[Bibliometric mapping of the application of artificial intelligence in nutrition: a visualization analysis]

To reveal the knowledge structure, research hotspots, and development trends in the field of nutrition through visual analysis of core literature on the application of artificial intelligence(AI), providing a reference for nutritional research.

Using bibliometric method, we retrieved relevant literature from the Web of Science database between 2016 and 2024, limiting the literature types to original articles or reviews. CiteSpace 6.4. R1 software was used for visual analysis, including keyword co-occurrence, clustering, timeline, and emergence analysis, to construct a knowledge graph and analyze author, institutional collaboration networks, and research themes.

A total of 1896 core papers were included, showing an accelerating growth trend in publication volume, entering a rapid growth phase after 2021, with a total growth rate of 485.7% and an average annual growth rate of 19.8%. Core authors included 78 individuals, accounting for 25.74% of all researchers, publishing 433 papers, which accounted for 22.84% of the total literature. Research institutions were mainly concentrated in China and the United States, with the University of California and Harvard University in the U. S. , and the Chinese Academy of Sciences and the Chinese Academy of Medical Sciences/Union Medical College in China, occupying core positions in the collaboration network. Keyword analysis revealed that keywords such as "machine learning""metabolic syndrome""gut microbiota" were high-frequency terms, accounting for 45.31% of the total word frequency. Keyword clustering analysis formed 11 thematic clusters, covering key application scenarios such as chronic disease risk assessment and personalized dietary interventions. The keyword burst analysis reveals evolving research priorities, shifting from disease-diet association studies to nutrition interventions, mental health, and technical standardization.

The findings indicate that AI applications in nutrition are advancing toward diversified and refined development. Future efforts should emphasize interdisciplinary collaboration to promote standardized and policy-driven implementation of these technologies.