Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future
Jonathan A. Tangsrivimol,
Ethan Schonfeld,
Michael Zhang,
Anand Veeravagu,
Timothy R. Smith,
Roger Härtl,
Michael T. Lawton,
Adham H. El-Sherbini,
Daniel M. Prevedello,
Benjamin S. Glicksberg,
Chayakrit Krittanawong
Affiliations
Jonathan A. Tangsrivimol
Division of Neurosurgery, Department of Surgery, Chulabhorn Hospital, Chulabhorn Royal Academy, Bangkok 10210, Thailand
Ethan Schonfeld
Department Biomedical Informatics, Stanford University School of Medicine, Palo Alto, CA 94305, USA
Michael Zhang
Department of Neurosurgery, Stanford University School of Medicine, Palo Alto, CA 94305, USA
Anand Veeravagu
Stanford Neurosurgical Artificial Intelligence and Machine Learning Laboratory, Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA 94305, USA
Timothy R. Smith
Department of Neurosurgery, Computational Neuroscience Outcomes Center (CNOC), Mass General Brigham, Harvard Medical School, Boston, MA 02115, USA
Roger Härtl
Weill Cornell Medicine Brain and Spine Center, New York, NY 10022, USA
Michael T. Lawton
Department of Neurosurgery, Barrow Neurological Institute (BNI), Phoenix, AZ 85013, USA
Adham H. El-Sherbini
Faculty of Health Sciences, Queen’s University, Kingston, ON K7L 3N6, Canada
Daniel M. Prevedello
Department of Neurological Surgery, The Ohio State University Wexner Medical Center and Jame Cancer Institute, Columbus, OH 43210, USA
Benjamin S. Glicksberg
Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
Chayakrit Krittanawong
Cardiology Division, New York University Langone Health, New York University School of Medicine, New York, NY 10016, USA
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.
