Modeling new trends in bone regeneration, using the BERTopic approach

Abstract

Aim: Bibliometric surveys are time-consuming endeavors, which cannot be scaled up to meet the challenges of ever-expanding fields, such as bone regeneration. Artificial intelligence, however, can provide smart tools to screen massive amounts of literature, and we relied on this technology to automatically identify research topics. Materials & methods: We used the BERTopic algorithm to detect the topics in a corpus of MEDLINE manuscripts, mapping their similarities and highlighting research hotspots. Results: Using BERTopic, we identified 372 topics and were able to assess the growing importance of innovative and recent fields of investigation such as 3D printing and extracellular vescicles. Conclusion: BERTopic appears as a suitable tool to set up automatic screening routines to track the progress in bone regeneration.