Large Language Models in Neurosurgery.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Computational Neuroscience

Background:

• Large language models (LLMs) are advanced neural networks trained on extensive text data.
• Models like ChatGPT utilize transformer architectures and deep learning for human-like text generation.
• The 'large' designation refers to a high number of parameters, enhancing pattern recognition in language.

Purpose of the Study:

• To explore the potential applications of LLMs, specifically ChatGPT, in neurosurgery.
• To assess the utility of LLMs in generating medical documentation and educational materials.
• To discuss the benefits and drawbacks of implementing LLMs in neurosurgical practice.

Main Methods:

• Utilizing ChatGPT, a prominent LLM, for text generation tasks.
• Training the model on a comprehensive corpus to ensure grammatical correctness and semantic relevance.
• Evaluating the model's output for accuracy and utility in generating surgical reports and notes.

Main Results:

• LLMs can generate grammatically correct and semantically meaningful text across various domains.
• ChatGPT demonstrated capability in producing detailed surgical reports for inter-team communication.
• The model can create comprehensive surgical notes beneficial for resident and medical student education.

Conclusions:

• Large language models offer significant potential as valuable tools within the field of neurosurgery.
• Applications include enhancing surgical reporting, streamlining documentation, and aiding medical education.
• Further discussion on the potential benefits and challenges of LLM integration in neurosurgery is warranted.