Development of a Neurosurgery Chatbot Using Large Language Models with Retrieval-Augmented Generation
Abstract
Patient education in neurosurgery is crucial for empowering patients to make informed decisions. This study introduces NeuroBot, a chatbot leveraging Large Language Models with Retrieval-Augmented Generation to provide accurate, relevant, and context-aware responses to patient inquiries. NeuroBot's knowledge base includes curated neurosurgery resources. Evaluation of NeuroBot revealed superior performance, achieving high accuracy (5.28/6), relevance (5.46/6), and completeness (2.61/3). NeuroBot demonstrates the potential of LLMs to transform patient education, offering personalized and accessible healthcare communication.
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