Introduction Artificial intelligence (AI) has rapidly emerged as a transformative force in biomedical research, driving advances in data interpretation, diagnostics, and therapeutic strategies. The objective of this study was to conduct a large-scale bibliometric analysis of AI-related biomedical publications indexed in PubMed between 1950 and 2024, with the aim of identifying temporal trends, global research contributions, and thematic focus areas. Methods A retrospective bibliometric analysis was performed on 76,722 PubMed-indexed articles. Data were retrieved using a Python-based pipeline integrating National Center for Biotechnology Information (NCBI) Entrez Programming Utilities and Biopython. Metadata-including PubMed Identifier (PMID), title, abstract, authors, publication date, journal, Medical Subject Headings (MeSH) terms, Digital Object Identifier (DOI), and country of origin-were extracted, cleaned, and compiled into a structured dataset. Articles were analyzed for completeness, publication patterns, geographic distribution, journal outlets, and thematic focus using MeSH keywords. Results AI-related publications remained scarce until 2015, after which output expanded exponentially, reaching 20,135 articles in 2024. The United States contributed the largest share (32.5%, n = 24,958), followed by England (22.5%) and Switzerland (n = 13,936). Thematic analysis revealed machine learning (n = 16,281), deep learning (n = 12,297), and neural networks (n = 7,117) as dominant areas, while AI ethics appeared in only 110 publications. Metadata completeness varied, with notable gaps in MeSH indexing (41.6%), abstracts (9%), and Digital Object Identifiers (DOI) (2.6%). Research was widely disseminated across more than 6,000 journals, with Sensors , Scientific Reports , and Public Library of Science ONE ( PLOS ONE) as leading outlets. Discussion The findings highlight AI’s transition from a peripheral topic to a core pillar of biomedical science, with rapid growth driven by technological advances and global health demands. Despite widespread adoption across multiple disciplines, gaps remain in ethical engagement and equitable global representation. Metadata inconsistencies also pose challenges for systematic synthesis and bibliometric analyses. Conclusion AI has become a central component of biomedical research, characterized by exponential growth, interdisciplinary adoption, and global expansion. However, limited attention to AI ethics and persistent disparities in research representation underscore the need for targeted policy, funding, and governance strategies. Continued bibliometric monitoring is essential to ensure responsible and inclusive integration of AI into clinical and research practice.
Al-Bimani et al. (Wed,) studied this question.