AIMC Topic: Neurosurgery

Clear Filters Showing 71 to 80 of 117 articles

Ethical Concerns of AI in Neurosurgery: A Systematic Review.

Brain and behavior
BACKGROUND: The relentless integration of Artificial Intelligence (AI) into neurosurgery necessitates a meticulous exploration of the associated ethical concerns. This systematic review focuses on synthesizing empirical studies, reviews, and opinion ...

Neurosurgery, Explainable AI, and Legal Liability.

Advances in experimental medicine and biology
One of the challenges of AI technologies is its "black box" nature, or the lack of explainability and interpretability of these technologies. This chapter explores whether AI systems in healthcare generally, and in neurosurgery specifically, should b...

Focusing a Bioethics Lens on the Development and Use of Artificial Intelligence in Medicine and Neurosurgery.

Advances in experimental medicine and biology
The rapid pace of development and application of digital technology and data science, including artificial intelligence (AI), is transforming our world. In this chapter, we address the question: "Is bioethics relevant to how we should develop, govern...

Comprehensive Overview of Computational Modeling and Artificial Intelligence in Pediatric Neurosurgery.

Advances in experimental medicine and biology
In this chapter, we give an overview of artificial intelligence tools and their use thus far in pediatric neurosurgery. We discuss different machine learning algorithms from a data-driven approach in order to guide clinicians and scientists as they a...

Large Language Models in Neurosurgery.

Advances in experimental medicine and biology
A large language model (LLM), in the context of natural language processing and artificial intelligence, refers to a sophisticated neural network that has been trained on a massive amount of text data to understand and generate human-like language. T...

Bayesian Neural Networks in Predictive Neurosurgery.

Advances in experimental medicine and biology
"Bayesian Neural Networks in Predictive Neurosurgery" explains both conceptually and theoretically the combination of statistical techniques for clinical prediction models, including artificial neural networks, Bayesian regression, and Bayesian neura...