Related Experiment Video
Updated: Sep 20, 2025
Surgical Training for the Implantation of Neocortical Microelectrode Arrays Using a Formaldehyde-fixed Human Cadaver Model
Published on: November 19, 2017
Artificial intelligence in neurosurgery: a systematic review of applications, model comparisons, and ethical
Jheremy S Reyes1, Vanshika N Lohia2, Timoteo Almeida2
1Center for Image-Guided Neurosurgery, Neurological surgery department, University of Pittsburgh Medical Center (UPMC), Pittsburgh, PA, USA. reyesbarretojs@upmc.edu.
Background:
Artificial Intelligence (AI) has emerged as a transformative tool in medicine, particularly addressing neurosurgical challenges such as complex anatomical delineation and intraoperative decision-making. Despite advancements in diagnostic and prognostic algorithms, obstacles including algorithmic bias, data privacy, and model interpretability continue to limit its widespread clinical adoption.
Objective:
This systematic review aims to evaluate the current applications of AI in neurosurgery, compare the performance of various AI models, and examine the ethical challenges associated with their integration into clinical practice.
Methods:
A systematic literature search was conducted in PubMed, Scopus, and Web of Science databases, following PRISMA guidelines. Studies from 2015 to 2025 focusing on AI applications in diagnostic, prognostic, surgical, and intraoperative neurosurgical contexts were included. Statistical outcomes, model performance metrics, and ethical considerations were analyzed.
Results:
Thirteen studies met the inclusion criteria. AI models, particularly ML and DL, demonstrated superior diagnostic accuracy (AUC > 0.90) and improved prognostic predictions by up to 15%. AI-assisted surgical planning enhanced precision and reduced complication rates by 10-20%. However, algorithmic bias, limited transparency, and lack of external validation remain key barriers to clinical adoption.
Conclusion:
AI improves diagnostic accuracy, prognostic predictions, and surgical precision while reducing complication rates. However, challenges such as bias, limited interpretability, and the need for external validation must be addressed for widespread clinical integration.
