ArticleActa neurochirurgica2020
Machine learning in neurosurgery: a global survey.
Article in Acta neurochirurgica, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
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Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Wide range of applications for machine-learning prediction models in orthopedic surgical outcome: a systematic review.Acta orthopaedica · 2021Pooled it
- Perceptions and knowledge of machine learning for paediatric related decision support in emergency care - A UK and Ireland network survey study of clinician leaders.PLOS digital health · 2026Article
- Individuation of predictive parameters of posterior cranial fossa decompression in pediatric patients with Chiari I malformation and autism spectrum disorder.Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery · 2026Article
- Artificial Intelligence in Oculoplastics: A Survey-Based Study on Provider Perspective.Cureus · 2025Article
- Exploring perspectives and boundaries in neurosurgical career pathways for generation Z in German-speaking countries.Brain & spine · 2025Article
- The age of Artificial Intelligence in neurosurgical practice and educational paradigms - Considerations from the EANS Ethico-Legal and Young Neurosurgeons' committees.Brain & spine · 2025Article
- The use of cloud based machine learning to predict outcome in intracerebral haemorrhage without explicit programming expertise.Neurosurgical review · 2024Article
- Pioneering Augmented and Mixed Reality in Cranial Surgery: The First Latin American Experience.Brain sciences · 2024Article
- Artificial Intelligence-Enhanced Neurocritical Care for Traumatic Brain Injury : Past, Present and Future.Journal of Korean Neurosurgical Society · 2024Article
- Integrating Augmented Reality in Spine Surgery: Redefining Precision with New Technologies.Brain sciences · 2024Review
- Understanding Physician's Perspectives on AI in Health Care: Protocol for a Sequential Multiple Assignment Randomized Vignette Study.JMIR research protocols · 2024Article
- Understanding Big Data in Neurosurgery.Advances in experimental medicine and biology · 2024Review
- Demonstrating the successful application of synthetic learning in spine surgery for training multi-center models with increased patient privacy.Scientific reports · 2023Article
- Artificial Intelligence in Neurosurgery: A State-of-the-Art Review from Past to Future.Diagnostics (Basel, Switzerland) · 2023Review
- Robot-assisted screw fixation in a cadaver utilizing magnetic resonance imaging-based synthetic computed tomography: toward radiation-free spine surgery. Illustrative case.Journal of neurosurgery. Case lessons · 2023Article
- Perceptions of Doctors in Saudi Arabia Toward Virtual Reality and Augmented Reality Applications in Healthcare.Cureus · 2023Article
- Attitudes of Anesthesiologists toward Artificial Intelligence in Anesthesia: A Multicenter, Mixed Qualitative-Quantitative Study.Journal of clinical medicine · 2023Article
- Limits on using the clock drawing test as a measure to evaluate patients with neurological disorders.BMC neurology · 2022Article
- Machine learning in clinical diagnosis, prognostication, and management of acute traumatic spinal cord injury (SCI): A systematic review.Journal of clinical orthopaedics and trauma · 2022Review
- Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRecent technological advances have led to the development and implementation of machine learning (ML) in various disciplines, including neurosurgery. Our goal was to conduct a comprehensive survey of neurosurgeons to assess the acceptance of and attitudes toward ML in neurosurgical practice and to identify factors associated with its use.
methodsThe online survey consisted of nine or ten mandatory questions and was distributed in February and March 2019 through the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS).
resultsOut of 7280 neurosurgeons who received the survey, we received 362 responses, with a response rate of 5%, mainly in Europe and North America. In total, 103 neurosurgeons (28.5%) reported using ML in their clinical practice, and 31.1% in research. Adoption rates of ML were relatively evenly distributed, with 25.6% for North America, 30.9% for Europe, 33.3% for Latin America and the Middle East, 44.4% for Asia and Pacific and 100% for Africa with only two responses. No predictors of clinical ML use were identified, although academic settings and subspecialties neuro-oncology, functional, trauma and epilepsy predicted use of ML in research. The most common applications were for predicting outcomes and complications, as well as interpretation of imaging.
conclusionsThis report provides a global overview of the neurosurgical applications of ML. A relevant proportion of the surveyed neurosurgeons reported clinical experience with ML algorithms. Future studies should aim to clarify the role and potential benefits of ML in neurosurgery and to reconcile these potential advantages with bioethical considerations.
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Registered trials
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