E-ISSN: 1019-5157
ISSN: 2651-5024
Research
A Bibliometric Analysis of 500 Articles on the Use of Artificial Intelligence in Neurosurgery
Emirhan Basmaz Ölmez✉ ,
Yasemin ADALI ,
Ümit Akın Dere
DOI: 10.5137/1019-5149.JTN.50475-25.3
Article in Press
Corresponding Author:
Emirhan Basmaz Ölmez (eolmez23@posta.pau.edu.tr)
Abstract
Aim
The aim of this bibliometric analysis was to systematically map and quantitatively evaluate the global scientific literature on artificial intelligence applications in neurosurgery.
Material and Methods
The Web of Science (WoS) Core Collection database was searched on October 8, 2025. The PubMed MeSH terminology was used to define search keywords. The search strategy combined (intelligence artificial OR computer reasoning OR AI OR machine intelligence OR computational intelligence OR computer vision system OR knowledge acquisition OR knowledge representation) with neurosurger using the AND operator. Articles published after 2000 and up to 2025 were restricted to the search. One thousand five hundred and sixty-six records were found and after screening the records using pre-specified inclusion and exclusion criterion, 500 eligible articles were used in the ultimate analysis. They were calculated using the open-source R package Biblioshiny.
Results
The 500 articles used within the study fell within the range of 2000 and 2025 and the growth rate was 20.46 per year showing the growing interest in the field. The most fruitful journals were World Neurosurgery, Neurosurgery, and Computational Neurosurgery. Thematic mapping showed that the focus on machine learning, deep learning, image segmentation, and surgical robotics increased in the recent years.
Conclusion**
The changing literature demonstrates that the use of AI in neurosurgery is not limited to clinical use but includes ethical, competence in practice, barriers, and reliability of the data used. These results indicate that there is the need to discuss AI implementation in neurosurgery in a multidisciplinary and ethically-based approach.
The aim of this bibliometric analysis was to systematically map and quantitatively evaluate the global scientific literature on artificial intelligence applications in neurosurgery.
Material and Methods
The Web of Science (WoS) Core Collection database was searched on October 8, 2025. The PubMed MeSH terminology was used to define search keywords. The search strategy combined (intelligence artificial OR computer reasoning OR AI OR machine intelligence OR computational intelligence OR computer vision system OR knowledge acquisition OR knowledge representation) with neurosurger using the AND operator. Articles published after 2000 and up to 2025 were restricted to the search. One thousand five hundred and sixty-six records were found and after screening the records using pre-specified inclusion and exclusion criterion, 500 eligible articles were used in the ultimate analysis. They were calculated using the open-source R package Biblioshiny.
Results
The 500 articles used within the study fell within the range of 2000 and 2025 and the growth rate was 20.46 per year showing the growing interest in the field. The most fruitful journals were World Neurosurgery, Neurosurgery, and Computational Neurosurgery. Thematic mapping showed that the focus on machine learning, deep learning, image segmentation, and surgical robotics increased in the recent years.
Conclusion**
The changing literature demonstrates that the use of AI in neurosurgery is not limited to clinical use but includes ethical, competence in practice, barriers, and reliability of the data used. These results indicate that there is the need to discuss AI implementation in neurosurgery in a multidisciplinary and ethically-based approach.
Keywords
Artificial intelligence
neurosurgery
robotics
bibliometric analysis
Biblioshiny