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Abstract

With medical technology innovation, robotic surgery has evolved from mechanical arm operations to AI-assisted decision-making, promoting deep integration of surgical medicine with engineering and computer science. This study employed CiteSpace software to conduct a bibliometric analysis of robotic surgical technology evolution literature from the Web of Science (2014-2024). Analysis of 520 publications revealed explosive growth from <5 annual papers (2014-2017) to 177 papers in 2024, representing a 3,540% increase. The dataset encompassed 2,968 authors, 1,957 institutions, and 266 journals across 77 countries/regions. The United States dominated with 191 publications (36.73%), followed by China (88, 16.92%) and the United Kingdom (71, 13.65%). The University of London emerged as the most productive institution (28 publications). Keyword burst analysis identified "artificial intelligence" (2019-2024) and "deep learning methods" (2022-2024) as dominant emerging themes. Computer science categories comprised >10% of publications, demonstrating strong interdisciplinary integration centered on surgery (31.54%) and biomedical engineering (12.31%). The field demonstrated clear evolution from basic instrument innovation to AI-driven, multi-disciplinary collaborative intelligent surgical systems, with Italy (centrality 0.18) and France (0.16) serving as critical knowledge brokers despite moderate publication volumes.

Keywords

Robotic surgery Artificial intelligence Bibliometric analysis Technology evolution Interdisciplinary research

Article Details

How to Cite
Liu, Y., & Wira Mohd Shafiei, M. (2025). From robotic arms to AI-assisted: the evolution and interdisciplinary integration of robotic surgery technology based on bibliometron . Future Technology, 4(3), 148–158. Retrieved from https://fupubco.com/futech/article/view/375
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References

  1. Ashrafian, H., Clancy, O., Grover, V., & Darzi, A. (2017). The evolution of robotic surgery: surgical and anaesthetic aspects. BJA: British Journal of Anaesthesia, 119(suppl_1), i72-i84.
  2. Bhandari, M., Zeffiro, T., & Reddiboina, M. (2020). Artificial intelligence and robotic surgery: current perspective and future directions. Current opinion in urology, 30(1), 48-54.
  3. Bokolo, A., Kamaludin, A., Romli, A., Raffei, A., Phon, D., Abdullah, A., & Shukor, N. (2020). Web of science. Journal of Research on Technology in Education, 52(1), 37-64.
  4. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for information Science and Technology, 57(3), 359-377.
  5. Chen, C. (2014). The citespace manual. College of Computing and Informatics, 1(1), 1-84.
  6. Chen, C. (2016). CiteSpace: a practical guide for mapping scientific literature. Nova Science Publishers Hauppauge, NY, USA.
  7. Dai, Z., Xu, S., Wu, X., Hu, R., Li, H., He, H., Hu, J., & Liao, X. (2022). Knowledge mapping of multicriteria decision analysis in healthcare: a bibliometric analysis. FRONTIERS IN PUBLIC HEALTH, 10, 895552.
  8. Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017). Dermatologist-level classification of skin cancer with deep neural networks. nature, 542(7639), 115-118.
  9. Hashimoto, D. A., Rosman, G., Rus, D., & Meireles, O. R. (2018). Artificial intelligence in surgery: promises and perils. ANNALS OF SURGERY, 268(1), 70-76.
  10. Kalan, S., Chauhan, S., Coelho, R. F., Orvieto, M. A., Camacho, I. R., Palmer, K. J., & Patel, V. R. (2010). History of robotic surgery. Journal of robotic surgery, 4, 141-147.
  11. Kwoh, Y. S., Hou, J., Jonckheere, E. A., & Hayati, S. (1988). A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Transactions on Biomedical Engineering, 35(2), 153-160.
  12. Liu, Y., Wu, X., Sang, Y., Zhao, C., Wang, Y., Shi, B., & Fan, Y. (2024). Evolution of surgical robot systems enhanced by artificial intelligence: A review. Advanced Intelligent Systems, 6(5), 2300268.
  13. Mascagni, P., Vardazaryan, A., Alapatt, D., Urade, T., Emre, T., Fiorillo, C., Pessaux, P., Mutter, D., Marescaux, J., & Costamagna, G. (2022). Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. ANNALS OF SURGERY, 275(5), 955-961.
  14. Masic, I., & Ferhatovica, A. (2012). Review of most important biomedical databases for searching of biomedical scientific literature. Donald School Journal of Ultrasound in Obstetrics and Gynecology, 6(4), 343-361.
  15. O'Sullivan, S., Nevejans, N., Allen, C., Blyth, A., Leonard, S., Pagallo, U., Holzinger, K., Holzinger, A., Sajid, M. I., & Ashrafian, H. (2019). Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. The International Journal of Medical Robotics and Computer Assisted Surgery, 15(1), e1968.
  16. Rees, M. (2012). Code of Conduct and Best Practice Guidelines for Journal Editors, 2011. Committee on Publication Ethics (COPE). In.
  17. Rusk, N. (2016). Deep learning. Nature Methods, 13(1), 35-35.
  18. Shademan, A., Decker, R. S., Opfermann, J. D., Leonard, S., Krieger, A., & Kim, P. C. (2016). Supervised autonomous robotic soft tissue surgery. Science translational medicine, 8(337), 337ra364-337ra364.
  19. Sheetz, K. H., Claflin, J., & Dimick, J. B. (2020). Trends in the adoption of robotic surgery for common surgical procedures. JAMA NETWORK OPEN, 3(1), e1918911-e1918911.
  20. Taylor, R. H., Simaan, N., Menciassi, A., & Yang, G.-Z. (2022). Surgical robotics and computer-integrated interventional medicine [scanning the issue]. Proceedings of the IEEE, 110(7), 823-834.
  21. Zhu, Z., Ng, D. W. H., Park, H. S., & McAlpine, M. C. (2021). 3D-printed multifunctional materials enabled by artificial-intelligence-assisted fabrication technologies. Nature Reviews Materials, 6(1), 27-47.