Employing large language models safely and effectively as a practicing neurosurgeon

Area of Science:

• Neurosurgery
• Artificial Intelligence
• Medical Informatics

Background:

• Large Language Models (LLMs) show promise in neurosurgery knowledge management and education.
• Clinical integration of LLMs in neurosurgery is an evolving area of research.
• Neurosurgeons are exploring the safe and effective deployment of LLM technology.

Purpose of the Study:

• To explore key considerations for the safe and efficacious deployment of LLMs in neurosurgical practice.
• To provide a framework for neurosurgeons evaluating LLM adoption.

Main Methods:

• Literature search of PubMed for studies on LLMs in neurosurgery.
• Review of papers for use cases, LLM selection criteria, and encountered challenges, including private health information processing.

Main Results:

• Core principles for LLM selection include technical aspects like access, context windows, multimodality, RAG, and performance benchmarks.
• Safety considerations and institutional support for LLM inference on private health data are discussed.
• A framework is presented for neurosurgeons considering LLM implementation.

Conclusions:

• LLMs offer significant opportunities to advance neurosurgical practice.
• Clinical adoption requires careful assessment of technical, ethical, and regulatory challenges.
• Thoughtful evaluation of model selection, deployment, and compliance is crucial for maximizing benefits and minimizing risks.