Journal Article

Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future

Tangsrivimol, Jonathan A., Lawton, Michael T., Smith, Timothy R., Schonfeld, Ethan, Härtl, Roger, El-Sherbini, Adham H., Veeravagu, Anand, Prevedello, Daniel M., Zhang, Michael, Krittanawong, Chayakrit, Glicksberg, Benjamin S.

2023

Overview

In recent years, there has been a significant surge in discussions surrounding artificial intelligence (AI), along with a corresponding increase in its practical applications in various facets of everyday life, including the medical industry. Notably, even in the highly specialized realm of neurosurgery, AI has been utilized for differential diagnosis, pre-operative evaluation, and improving surgical precision. Many of these applications have begun to mitigate risks of intraoperative and postoperative complications and post-operative care. This article aims to present an overview of the principal published papers on the significant themes of tumor, spine, epilepsy, and vascular issues, wherein AI has been applied to assess its potential applications within neurosurgery. The method involved identifying high-cited seminal papers using PubMed and Google Scholar, conducting a comprehensive review of various study types, and summarizing machine learning applications to enhance understanding among clinicians for future utilization. Recent studies demonstrate that machine learning (ML) holds significant potential in neuro-oncological care, spine surgery, epilepsy management, and other neurosurgical applications. ML techniques have proven effective in tumor identification, surgical outcomes prediction, seizure outcome prediction, aneurysm prediction, and more, highlighting its broad impact and potential in improving patient management and outcomes in neurosurgery. This review will encompass the current state of research, as well as predictions for the future of AI within neurosurgery.

• Publisher: MDPI AG,MDPI
• Subject: Artificial intelligence / artificial intelligence (AI) / artificial Neural Networks (ANN) / Brain cancer / Cancer / Computational linguistics / Convolutional Neural Networks (CNN) / deep learning (DL) / Language processing / Machine learning / machine learning (ML) / Mutation / Natural language interfaces / Nervous system / Neural networks / Oncology, Experimental / Recurrent Neural Networks (RNN) / Review / Smith, T.R / Surgery