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Development of a Neurosurgery Chatbot Using Large Language Models with Retrieval-Augmented Generation.

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This study introduces NeuroBot, an AI chatbot for neurosurgery patient education. NeuroBot uses advanced language models to deliver accurate and relevant information, enhancing patient understanding and decision-making.

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Area of Science:

  • Neurosurgery
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Patient education is vital in neurosurgery for informed decision-making.
  • Current methods may lack accessibility or personalization.
  • The need for innovative educational tools is increasing.

Purpose of the Study:

  • To introduce NeuroBot, an AI-powered chatbot for neurosurgery patient education.
  • To leverage Large Language Models (LLMs) with Retrieval-Augmented Generation (RAG) for accurate information delivery.
  • To evaluate the performance of NeuroBot in responding to patient inquiries.

Main Methods:

  • Developed NeuroBot, a chatbot utilizing LLMs and RAG.
  • Integrated a curated knowledge base of neurosurgery resources into NeuroBot.
  • Conducted an evaluation of NeuroBot's accuracy, relevance, and completeness.

Main Results:

  • NeuroBot demonstrated high performance in evaluations.
  • Achieved high scores for accuracy (5.28/6) and relevance (5.46/6).
  • Received a strong score for completeness (2.61/3).

Conclusions:

  • NeuroBot shows significant potential for transforming neurosurgery patient education.
  • LLM-powered chatbots can offer personalized and accessible healthcare communication.
  • The technology can empower patients with reliable neurosurgery information.