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

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 framework integrated 2,200 multimodal knowledge units through dynamic semantic indexing. EndoQ demonstrated statistically significant improvements across all evaluation metrics compared to general purpose LLMs: accuracy (4.45 +/- 0.96), clinical relevance (4.59 +/- 0.8), completeness (4.27 +/- 0.83), professionalism judgment (4.45 +/- 1.06), and language fluency (4.86 +/- 0.47), as measured on a 5-point Likert scale. CONCLUSION: This proposed framework improves educational outcomes through precise and context-aware knowledge delivery. Furthermore, it represents a scalable and transferable model for AI-enhanced clinical training across medical specialties, significantly advancing competency-based pedagogy in dental education.