Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery

Aug 5, 2026

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Neurosurgeons complete extensive technical and clinical training to handle complex information and high-stakes decisions across preoperative, intraoperative, and postoperative stages. Recent advances in artificial intelligence (AI)—including machine learning (ML) and deep learning (DL)—are increasingly applied in neurosurgery. AI can enhance diagnostic and prognostic performance while supporting decision-making during procedures, improving patient outcomes and surgical planning. Reported progress also includes reduced complications through interventional and non-interventional therapeutic integration.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Introduction

Article Overview and Background

AI, ML, and DL in Neurosurgery

Potential Benefits for Outcomes and Decision-Making

Keywords and Abbreviations

World Neurosurgery: X 23 (2024) 100301 Contents lists available at ScienceDirect World Neurosurgery: X journal [homepage:](homepage: www.journals.elsevier.com/world-neurosurgery-x)[ www.journals.elsevier.com/world-neurosurgery-x](homepage: www.journals.elsevier.com/world-neurosurgery-x) | Recent Outcomes and Challenges of Artificial Intelligence, Machine Learning, and Deep Learning in Neurosurgery | | | | | --- | --- | --- | --- | | Wireko Andrew Awuaha, **, Favour Tope Adebusoyea, *, Jack Wellington b, Lian David c, Abdus Salam d, Amanda Leong Weng Yeee, Edouard Lansiauxf, Rohan Yarlagaddag, Tulika Gargh, Toufik Abdul-Rahman a, Jacob Kalmanovichi, Goshen David Miteuj, Mrinmoy Kundu k, Nikitina Iryna Mykolaivna a
a Sumy State University, Sumy, United Kingdom
b Cardiff University School of Medicine, Cardiff University, Wales, United Kingdom c Norwich Medical School, University of East Anglia, United Kingdom d Department of Surgery, Khyber Teaching Hospital, Peshawar, Pakistan e University of Malaya, Kuala Lumpur, Malaysia
f Lille University School of Medicine, Lille, France
g Rowan University School of Osteopathic Medicine, Stratford, NJ, USA h Government Medical College and Hospital Chandigarh, India
i Drexel University College of Medicine, Philadelphia, PA, USA
j School of Bioscience, University of Nottingham, England, United Kingdom k Institute of Medical Sciences and SUM Hospital, Bhubaneswar, India | | | | | A R T I C L E I N F O | | A B S T R A C T | | | Keywords: Neurosurgery Artificial intelligence Machine learning Deep learning Virtual reality | | Neurosurgeons receive extensive technical training, which equips them with the knowledge and skills to specialise in various fields and manage the massive amounts of information and decision-making required throughout the various stages of neurosurgery, including preoperative, intraoperative, and postoperative care and recovery. Over the past few years, artificial intelligence (AI) has become more useful in neurosurgery. AI has the potential to improve patient outcomes by augmenting the capabilities of neurosurgeons and ultimately improving diagnostic and prognostic outcomes as well as decision-making during surgical procedures. By incorporating AI into both interventional and non-interventional therapies, neurosurgeons may provide the best care for their patients. AI, machine learning (ML), and deep learning (DL) have made significant progress in the field of neurosurgery. These cutting-edge methods have enhanced patient outcomes, reduced complications, and improved surgical planning. | | Abbreviations: AI, Artificial Intelligence; ML, Machine Learning; DL, Deep Learning; VR, Virtual Reality; ANNs, Artificial Neural Networks; MRI, Magnetic Resonance Imaging; MESCC, Metastatic Epidural Spinal Cord Compression; CT, Computed Tomography; CNN, Convolutional Neural Network; EEG, Electroencephalography; SVM, Support Vector Machine; k-NN, k- Nearest Neighbor; TLE, Temporal Lobe Epilepsy; iEEG, intracranial Electroencephalography; PLVs, Phase Locking Values; ATL, Anterior Temporal Lobectomy; ICH, Intracerebral Hemorrhage; LVO, Large Vessel Occlusion; IAs, Intracranial Aneurysms; AnOA, Aneurysm Occlusion Assistant; 3D, 3-dimensional; ICG VA, Indocyanine Green Video Angiography; EC-IC, Extracranial and Intracranial; CV, Computer Vision; GBM, Glioblastoma Multiformes; LGGs, Low-Grade Gliomas; HGGs, High-Grade Gliomas; DNNs, Deep Neural Networks; EHR, Electronic Health Record; XG, extreme gradient; SGD, stochastic gradient descent; DenseNet, densely connected convolutional network; SSI, surgical site infection; SOZ, seizure onset zone; DNN, deep neural network. * Corresponding author. Faculty of Medicine, Sumy State University, Sanatorna St, 31, Sumy, Sumy Oblast, 40000, Ukraine. ** Corresponding author. E-mail addresses: [andyvans36@yahoo.com](andyvans36@yahoo.com) (W.A. Awuah), [Favouradebusoye@gmail.com](Favouradebusoye@gmail.com), [F.adebuso

FAQs

How can AI improve outcomes in neurosurgery according to the article abstract?

AI can improve patient outcomes by augmenting neurosurgeons’ capabilities, supporting diagnostic and prognostic improvements, and improving decision-making during surgical procedures.

Which neurosurgical stages are mentioned as requiring substantial information and decisions?

The abstract highlights preoperative, intraoperative, and postoperative care and recovery as stages involving massive information and decision-making.

What progress do AI, ML, and DL bring to neurosurgery in the provided text?

The text states these methods have enhanced patient outcomes, reduced complications, and improved surgical planning, and can be incorporated into both interventional and non-interventional therapies.

Aug 5, 2026

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