ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon

Abstract Artificial intelligence (AI) is rapidly transforming health care, with significant implications for neurosurgery. This essay provides a focused overview of contemporary and emerging AI models relevant to neurosurgery, with particular emphasis on natural language processing (NLP) tools such as large language models (LLMs) and retrieval augmented generation systems. We present a framework for conceptualizing the AI–user relationship, emphasizing a collaborative model that promotes iterative refinement of queries and responses. The paper offers guidance on AI model selection for various neurosurgical tasks, highlighting the strengths of different AI types such as NLP models and machine learning algorithms. We introduce prompt engineering as a critical skill for neurosurgeons, providing practical tips and examples to optimize AI interactions. The review also discusses current limitations of AI in neurosurgery, including dataset biases and ethical considerations. By addressing these key areas, this article serves as a practical guide for neurosurgeons at all career stages to effectively integrate AI tools into their work, ultimately enhancing patient care, research capabilities, and educational practices in the field of neurosurgery.

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Publication Details

Journal
Journal of Neurological Surgery Part B Skull Base
Published
2025-08-19
DOI
https://doi.org/10.1055/a-2678-8460
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon

Visish Mani Srinivasan, Daniel A. Donoho, Ali S. Haider, Christopher S. Graffeo et al.
Journal of Neurological Surgery Part B Skull Base
Artificial Intelligence in Healthcare and Education
article

ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon

Visish Mani Srinivasan, Daniel A. Donoho, Ali S. Haider, Christopher S. Graffeo, Kishore Balasubramanian, Christopher Janssen, Nicholas Sader
article en

Abstract

Abstract Artificial intelligence (AI) is rapidly transforming health care, with significant implications for neurosurgery. This essay provides a focused overview of contemporary and emerging AI models relevant to neurosurgery, with particular emphasis on natural language processing (NLP) tools such as large language models (LLMs) and retrieval augmented generation systems. We present a framework for conceptualizing the AI–user relationship, emphasizing a collaborative model that promotes iterative refinement of queries and responses. The paper offers guidance on AI model selection for various neurosurgical tasks, highlighting the strengths of different AI types such as NLP models and machine learning algorithms. We introduce prompt engineering as a critical skill for neurosurgeons, providing practical tips and examples to optimize AI interactions. The review also discusses current limitations of AI in neurosurgery, including dataset biases and ethical considerations. By addressing these key areas, this article serves as a practical guide for neurosurgeons at all career stages to effectively integrate AI tools into their work, ultimately enhancing patient care, research capabilities, and educational practices in the field of neurosurgery.

Journal of Neurological Surgery Part B Skull Base
Children's National (US), Texas College (US), Rice University (US), University of Pennsylvania (US), University of Oklahoma (US), Texas A&M University (US)
Openalex Percentile: Top 85%
Artificial Intelligence in Healthcare and Education
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ChatNSG: An Overview of Contemporary and Emerging Artificial Intelligence Models for the Neurosurgeon — Visish Mani Srinivasan, Daniel A. Donoho, et al. · Journal of Neurological Surgery Part B Skull Base (2025) | TGRS Research Map | TGRS