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- In the last five years, there has been an accelerated growth in the scientific production about 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.). - The methodological approaches belong to the scientific fields of bibliometrics and topic modeling. 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 the most likely to be investigated in the future. – Results show 24 topics about future trends in literature review connecting the field of AI and Healthcare. - This bibliometric review of the literature on artificial intelligence and healthcare allows identifying of some privileged areas of attention by scholars of different disciplines. However, it also reveals the limits of hard clustering techniques, as demonstrated by the presence of some keywords in several groups. The numerous existing reviews must be integrated by reviews based on Topic Modeling techniques, which make it possible to identify topics, historical trends (classical and emerging topics), associations between the documents and to predict, on a probabilistic basis, which scientific fields will be most likely to see development in the future.
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Stored only when both papers are in the corpus — 15 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.