Bibliometric Analysis and Topic Modeling of the Literature on Artificial Intelligence in Healthcare

In the last 5 years, there has been accelerated growth in scientific production on the subject of artificial intelligence and healthcare by scholars of the most diverse disciplines. Recently, the scientific corpus has been enriched with considerable literature reviews ranging from the overview of large collections of scientific documents to the recognition of the state of knowledge on specific aspects (e.g., in the medical field, ophthalmology, cardiology, nephrology, etc.). Following a bibliometric analysis of the literature on the subject, conducted on a vast collection of scientific contributions, we also searched for the “latent” themes in the semantic structures of these documents, identified the relationships between them, and recognized those most likely to be investigated in the future. The methodological approach is located in the scientific fields of relational bibliometry and content analysis. The results of the bibliometric analysis are presented in terms of interactive maps of the association of the contributions based on bibliographic coupling and, subsequently, the co-occurrence of author keywords.