Application of artificial intelligence in neurosurgical practice: Technologies and challenges
DOI:
https://doi.org/10.67572/6tvzhc60Keywords:
artificial intelligence, neurosurgery, machine learning, deep learning, neuro-oncology, spinal surgery, ethicsAbstract
Introduction: Neurosurgery is a key area for the application of new technological innovations, including artificial intelligence (AI), which holds significant potential to transform diagnostic accuracy, surgical precision, and personalized medical care. Despite the considerable progress in the development of these technologies, their widespread application in clinical practice faces several challenges, including issues with data quality, difficulties in interpreting algorithms, and ethical concerns that require careful consideration. This review analyzes the current state of artificial intelligence in neurosurgery, focusing on its primary applications, benefits, limitations, and challenges.
Methods: This literature review summarizes current research on artificial intelligence in neurosurgery, with a particular focus on peer-reviewed studies published between 2020 and 2025. An extensive review of data from databases such as PubMed, Scopus, and Web of Science was conducted using terms such as “artificial intelligence,” “neurosurgery,” and related subspecialties.
Results: The applications of artificial intelligence in neurosurgery are expanding rapidly. In neuro-oncology, AI-based systems demonstrate high accuracy in detecting and classifying tumors. In vascular neurosurgery, AI assists with aneurysm detection and endovascular navigation, improving treatment efficiency. Intraoperatively, AI-based tools support surgeons by enhancing precision, reducing operation time, and predicting potential complications. Systems that utilize predictive models help forecast postoperative outcomes and assess patient risk. Key challenges for AI implementation include data heterogeneity and the integration of algorithms into existing clinical workflows. Additionally, ethical issues, such as data privacy and potential algorithmic bias, pose barriers to the broader, more effective deployment of these technologies in practice.
Conclusion: Artificial intelligence has the potential to transform neurosurgery by significantly improving diagnostic accuracy, surgical outcomes, and educational methods. With its continued development, collaboration between AI and experienced clinicians will be crucial for the full realization of its benefits in neurosurgical practice.
Keywords: artificial intelligence, neurosurgery, machine learning, deep learning, neuro-oncology, spinal surgery, ethics.
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Copyright (c) 2026 Dimitar Slavkov, Svetoslava Troyanova-Slavkova, Asen Hadzhiyanev, Vasil Karakostov (Author)
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