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Updated: Dec 11, 2025

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Machine learning in neurosurgery: a global survey.

Victor E Staartjes1,2,3, Vittorio Stumpo4,5, Julius M Kernbach6

  • 1Machine Intelligence in Clinical Neuroscience (MICN) Lab, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Frauenklinikstrasse 10, 8091, Zurich, Switzerland. victor.staartjes@gmail.com.

Acta Neurochirurgica
|August 20, 2020
PubMed
Summary

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A survey found that 28.5% of neurosurgeons use machine learning (ML) in practice, with common uses in outcome prediction and imaging. Further research is needed to clarify ML

Area of Science:

  • Neurosurgery
  • Artificial Intelligence
  • Medical Technology

Background:

  • Technological advancements have enabled machine learning (ML) integration into neurosurgery.
  • Assessing neurosurgeons' acceptance and attitudes toward ML is crucial for its adoption.
  • Identifying factors influencing ML use in neurosurgical practice is essential.

Purpose of the Study:

  • To survey neurosurgeons globally regarding their acceptance and use of ML.
  • To identify factors associated with the adoption of ML in neurosurgery.
  • To understand current applications of ML in clinical practice and research.

Main Methods:

  • An online survey was distributed to neurosurgeons via the European Association of Neurosurgical Societies (EANS) and the Congress of Neurosurgeons (CNS).
Keywords:
Artificial intelligenceGlobalMachine learningNeurosurgeryTechnologyWorldwide survey

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  • The survey targeted 7280 neurosurgeons, collecting data on ML usage in clinical practice and research.
  • Response rate was 5% (362 responses), with data primarily from Europe and North America.

    • Main Results:

    • 28.5% of respondents use ML in clinical practice, and 31.1% use it in research.
    • ML adoption varied globally, with higher rates in Asia/Pacific and Africa (limited data).
    • Common ML applications include outcome prediction, complication analysis, and image interpretation; no predictors for clinical use were found, but academic settings and subspecialties predicted research use.

    Conclusions:

    • A significant proportion of neurosurgeons have experience with ML algorithms in their practice.
    • The study provides a global overview of ML applications in neurosurgery.
    • Future research should explore ML's role, benefits, and bioethical implications in neurosurgery.