Big Data in Neurosurgery: A Guideline on Data Structures, Machine Learning Models, and Ethical Considerations

Authors

Rohin Singh, Department of Neurosurgery, University of Rochester, Rochester, New York, USA.
George Kassis, Department of Neurosurgery, University of Rochester, Rochester, New York, USA.
Omar Sbaih, Department of Neurosurgery, University of Rochester, Rochester, New York, USA.
Herman Li, Department of Neurosurgery, University of Rochester, Rochester, New York, USA.
Shane Shahrestani, Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Michael T. Lawton, Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA.
Oren Gottfried, Department of Neurosurgery, Duke University, Durham, North Carolina, USA.
Mohamad Bydon, Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA.
Jonathan J. Stone, Department of Neurosurgery, University of Rochester, Rochester, New York, USA.

Document Type

Article

Abstract

Artificial intelligence (AI) is reshaping neurosurgery, offering unprecedented opportunities to enhance diagnostics, personalize treatment, and predict outcomes. At the heart of this transformation is the ability to effectively harness big data (BD) within the electronic medical record. Understanding these data structures is essential for making sense of the vast volumes of information generated in modern neurosurgical practice. Equally important are the machine learning (ML) models driving these advancements. From supervised learning and convolutional neural networks to generative AI, these tools are already making a mark in areas such as brain tumor segmentation and spine surgery outcome predictions. Their versatility highlights the potential of ML to complement clinical expertise and streamline decision-making in neurosurgery. However, adopting BD and ML also brings ethical challenges that cannot be ignored. Bias in algorithms threatens to reinforce health disparities, whereas concerns about data privacy demand vigilance in handling sensitive patient information. In addition, the question of liability looms large as ML increasingly influences clinical decisions. The aim of the study was to provide a roadmap for neurosurgeons navigating the evolving intersection of BD, ML, and ethical responsibility in the AI era.

Publication Date

8-25-2025

Publication Title

Operative neurosurgery (Hagerstown, Md.)

E-ISSN

2332-4260

PubMed ID

40853158

Digital Object Identifier (DOI)

10.1227/ons.0000000000001751

Recommended Citation

Singh, Rohin; Kassis, George; Sbaih, Omar; Li, Herman; Shahrestani, Shane; Lawton, Michael T.; Gottfried, Oren; Bydon, Mohamad; and Stone, Jonathan J., "Big Data in Neurosurgery: A Guideline on Data Structures, Machine Learning Models, and Ethical Considerations" (2025). Neurosurgery. 2245.
https://scholar.barrowneuro.org/neurosurgery/2245