Development and evaluation of a retrieval-augmented large language model framework for enhancing endodontic education

作者：Xiaowei Xu, Siyi Liu, Lin Zhu, Yunzi Long, Zeng Yin, Xudong Lü, Jiao Li, Yanmei Dong · 发表于：International Journal of Medical Informatics · 年份：2025 · DOI：10.1016/j.ijmedinf.2025.106006 · 被引用次数：19 · 研究领域：Artificial Intelligence in Healthcare and Education、Topic Modeling、Multimodal Machine Learning Applications

BACKGROUND: Integrating domain-specific knowledge into large language models (LLMs) remains a critical challenge in medical education. In dental specialties such as endodontics, effective learning requires access to both textual clinical evidence and visual procedural demonstrations. However, generic LLMs often produce content that lacks clinical accuracy, contextual grounding, or pedagogical clarity, thereby limiting their applicability in specialized training environments. OBJECTIVE: To develop and evaluate a Retrieval-Augmented Generation (RAG)-enhanced LLMs framework that addresses the challenge of integrating domain-specific knowledge in AI-driven endodontic education. METHOD: We present Endodontics-KB, a multimodal knowledge integration platform that combines evidence-based dental literature (e.g., textbooks, clinical guidelines) with visual instructional materials (e.g., procedural videos) through a hierarchical RAG architecture. The system's core component, the EndoQ chatbot, utilizes LLMs augmented with multimodal dental datasets to enable context-aware clinical reasoning. Benchmarking was conducted against three general-purpose LLMs: GPT-4, Qwen2.5, and DeepSeek R1, using a structured question bank comprising 11 expert-validated endodontic questions. Two domain experts performed a blinded evaluation across five performance dimensions: clinical accuracy, contextual relevance, completeness, decision-making professionalism, and communication fluency. RESULTS: The frame...