Artificial Intelligence and Nutrition: A Bibliometric Analysis of Global Research Trends and Developments

Background: The integration of Artificial Intelligence (AI) into the field of nutritional science has gained considerable momentum in recent years, offering promising opportunities for the advancement of personalized dietary assessment, nutritional epidemiology, food safety monitoring, and public health surveillance. Despite the rapidly expanding volume of scholarly output in this domain, a comprehensive characterization of global publication trends, influential contributors, international collaboration patterns, and thematic developments within this field remains limited.

Aims: This study aimed to systematically analyze the research landscape at the intersection of AI and nutrition through bibliometric methodology, with the objectives of identifying publication trends, prolific authors, highly cited journals, leading contributing countries and institutions, and emerging thematic research foci.

Materials and Methods: A bibliometric analysis was conducted employing data systematically retrieved from the Scopus database on 19 July 2024. The search covered publications indexed between 2013 and 2023. An initial corpus of 1,167 documents was retrieved, from which 350 publications were retained following the application of predefined eligibility criteria encompassing subject area, language, document type, and topical relevance. VOSviewer and Microsoft Excel were employed to examine publication trends, citation patterns, international collaboration networks, and keyword co-occurrence relationships.

Results: The analysis revealed a substantial and progressive increase in scientific output during the final five years of the study period, reflecting the accelerating global interest in AI applications within nutritional research. The United States, China, and India emerged as the most productive contributing countries. The most prominent publication outlets included Nutrients, the Journal of Nutrition, and the EFSA Journal. Principal research themes involved AI-assisted dietary assessment, public health nutrition, machine learning applications in chronic disease prediction, food safety and quality control, and the ethical governance of nutritional data systems. The most frequently occurring keywords identified through co-occurrence analysis included “nutrition,” “artificial intelligence,” “dietary intake,” and “machine learning”.

Conclusions: The findings demonstrate the rapid growth and progressively multidisciplinary character of research at the interface of AI and nutrition. The field is distinguished by expanding international collaborative networks, diversifying thematic developments, and the deepening integration of AI technologies across the spectrum of nutritional science. These results provide valuable insights into the intellectual structure of the field and may serve to inform future research prioritization, evidence-based policy development, and innovation in AI-driven nutritional applications.