Neurosurgery is experiencing the impact of Artificial Intelligence (AI) in the form of improved diagnostic efficiency, procedural dexterity, and postsurgical management. This narrative review aims to discuss the numerous engagements of AI in the broad subdisciplinary areas of neurosurgery, such as neuro-oncology, functional neurosurgery, vascular neurosurgery, spinal neurosurgery, and Traumatic Brain Injury (TBI) care. The article pays particular attention to the application of machine learning algorithms and topic modeling for more accurate tumour grading, potential prediction of surgical outcomes for each patient and more appropriate patient stratification. Through early diagnosis in diagnostic imaging and individualised treatment regimens, technology—particularly AI— provides decisive information for constructive, real-time intraoperative data analysis. Additionally, the extension of AI applications in the telemedicine system helps to increase the availability of specialised care in relevant areas. While it is important to appreciate the risk of dependency on technology, this does not eliminate the potential for integrating such AI tools with surgical knowledge to continue improving patient care and outcomes. Current and future trends in the practical application of AI in neurosurgery include deep machine learning for neurosurgical planning and individualised patient data/targeted therapy. In general, AI plays an important role in enhancing neurosurgery while optimising the quality and outcomes of patient care.
Singh et al. (Mon,) studied this question.
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