Global trends and hotspots in the learning curves of robotic-assisted surgery: a bibliometric and visualization analysis.

Abstract

In recent years, there has been a substantial increase in the number of research papers published in the field of robotic-assisted surgery (RAS). Nevertheless, systematic analyses focusing on the key hotspots associated with the learning curves (LCs) of RAS, global collaboration models, and future trends remain relatively limited. This study employed bibliometric methods to conduct a comprehensive search and analysis of papers on the LC of RAS published in the Web of Science Core Collection between 2005 and 2025. A visual analysis was performed across multiple dimensions, including countries, institutions, sources, and authors. The results revealed an upward trend in the number of publications, with a peak observed in 2024. The United States ranked first in terms of publication volume, while Yonsei University emerged as the most productive institution. Mottrie Alexandre contributed to the highest number of publications, and Dindo d received the highest number of citations. Frequently occurring keywords included "outcome", "experience", "minimally invasive surgery", "revision", and "laparoscopic surgery". Clustering keywords were associated with "rectal cancer", "en-y gastric bypass", "transoral robotic surgery", "spine surgery", and "endometrial cancer". Furthermore, the top five keywords with the strongest citation bursts were "laparoscopic radical prostatectomy", "total mesorectal excision", "da vinci", "prostatectomy", and "mrc clasicc trial". This study offers valuable insights into the future development of this field and supports further exploration and innovation.