Research on artificial intelligence in stress and obesity: A bibliometric analysis using scopus, web of science, PubMed
Pooja Ramakrishna Pandit, Sreedhar Dharmagadda, Amit Kumar Goyal, Rajesh Mahadeva, Pradeep Manohar Muragundi, Shwetha Tumkur ShivakumarObjective
Stress and obesity are major health concerns affecting individuals worldwide. Artificial Intelligence (AI) is being designed to identify and address these health challenges more precisely. Therefore, this study aimed to conduct a bibliometric analysis to investigate the role of AI in stress and obesity.
Methods
A bibliometric analysis was conducted using Scopus, PubMed, and Web of Science databases. Relevant studies were identified using predefined search strategies with the Boolean operators OR and AND across the selected databases. The retrieved data were analyzed using Biblioshiny software.
Results
A total of 1921 studies were included in this analysis. The evaluation outcomes were categorized into documents, authors, publications, funding organizations, countries, keyword analysis, collaboration networks, and applications. Based on these results, the top 10 most-cited studies were identified, and the most significant outcomes were highlighted.
Conclusion
The findings provide comprehensive insights into the research landscape of AI in stress and obesity, highlighting rapid growth in publication output after 2020, increasing international collaboration, and the expanding role of AI-driven digital health solutions.