Adoption of Artificial Intelligence Technologies in Biomedical Research for Major Diseases: A Bibliometric Analysis

Abstract

The integration of artificial intelligence technologies in biomedical research has significantly advanced the study of diseases. This contribution presents a bibliometric analysis of scientific publications on applying machine learning, transformers, and generative artificial intelligence in studying five major diseases: cancer, cardiovascular disease, diabetes, mental health disorders, and COVID-19. We analyzed data from the Scopus database from 2012 to 2024 to assess the scientific output, the growth rate of publications in the early years of adoption of the technologies, and the countries pioneering the use of these technologies in diseaseassociated research. In addition, this study quantifies the use of these artificial intelligence techniques in research for different application areas of the biomedical field worldwide, highlighting various adoption patterns and the scientific impact of associated publications. Our results reveal that machine learning is widely used in surgical applications, while transformers have gained prominence in diagnostics, mainly through advanced text processing and image analysis. For its part, generative artificial intelligence is increasingly used in applications to optimize operational processes. This study highlights the transformative impact of emerging artificial intelligence technologies in biomedical research and provides insights into their roles and potential for improving healthcare. Furthermore, we propose a scalable framework for largescale scientific data analysis to guide future research trajectories and explore new applications in the biomedical sector.

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