Department of Medicine & Surgery, Histology & Embryology Lab, University of Parma, Parma, 43126, Italy.
Regen Med. 2023 Sep;18(9):719-734. doi: 10.2217/rme-2023-0096. Epub 2023 Aug 14.
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. We used the BERTopic algorithm to detect the topics in a corpus of MEDLINE manuscripts, mapping their similarities and highlighting research hotspots. 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. BERTopic appears as a suitable tool to set up automatic screening routines to track the progress in bone regeneration.
文献计量调查是一项耗时的工作，无法扩展规模以应对不断扩大的领域（如骨再生）的挑战。然而，人工智能可以提供智能工具来筛选大量文献，我们依靠这项技术自动识别研究课题。我们使用 BERTopic 算法来检测 MEDLINE 手稿语料库中的主题，绘制它们的相似性并突出研究热点。使用 BERTopic，我们确定了 372 个主题，并能够评估 3D 打印和细胞外囊泡等创新和最近研究领域日益重要的地位。BERTopic 似乎是一个合适的工具，可以建立自动筛选程序来跟踪骨再生的进展。
