Trends in AI-based diagnosis and intervention of metabolic diseases: a bibliometric analysis of the literature from 2000 to 2024 - Chen, Biting; Wang, Yinlu; Xie, Xuhuan; Fan, Boyan; Zhang, Wen; Sun, Guixiang (Frontiers in Medicine, 2025)
BackgroundIn recent years, the global prevalence of metabolic diseases has continued to rise, emerging as a major public health concern worldwide. Artificial intelligence (AI), with its advanced capabilities in data analysis, pattern recognition, and predictive reasoning, has demonstrated remarkable potential in enhancing the diagnosis, management, and prevention of metabolic disorders.MethodsLiterature searches were conducted in the WOSCC and Scopus databases, followed by database merging and deduplication using R scripts, resulting in a final corpus of 1,059 publications for analysis. Bibliometric analysis tools, including VOSviewer and CiteSpace, were subsequently employed to visualize and quantitatively assess the distribution of publications by country and institution, prolific authors, influential journals, citation networks, and emerging research keywords.ResultsResearch on AI in metabolic diseases has experienced explosive growth, with publication output increasing by 394% over the past 5 years, paralleling the broader expansion of AI technologies. China, the United States, and the United Kingdom have emerged as the leading contributors in this domain, with China contributing the largest share (21.87%), followed by the United States (17.10%). Among high-output institutions, Institutions from China contributed the most publications (298), while Harvard Medical School in the United States demonstrates the strongest academic influence. Nieuwdorp M stands out as the most prolific and highly cited author, with Kupusinac and Aleksandar also recognized for their significant contributions to the field. Scientific Reports ranks as the most productive journal, whereas Atherosclerosis is identified as one of the most authoritative journals among high-output publications. The co-occurrence of keywords such as “machine learning,” “deep learning,” “data mining,” “metabolomics,” and “diagnosis” reveals the application of artificial intelligence and advanced data processing techniques in metabolic diseases.DiscussionThis 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.