A Knowledge Graph-Based Intelligent Guidance System for Competency-Oriented Anesthesiology Residency Training: Design and Evaluation
Abstract
Abstract Background: Anesthesiology encompasses a complex and extensive knowledge domain that spans the entire perioperative continuum. Standardized residency training plays a critical role in developing clinical competence among junior physicians; however, current training approaches face several persistent challenges. Residents often acquire knowledge through fragmented learning resources, standardized bedside teaching remains difficult to implement consistently, and systematic training for critical event management is challenging to achieve. Furthermore, existing intelligent educational systems largely depend on cloud-based large language models, raising concerns regarding patient data privacy and limiting their deployment in clinical environments. This study aimed to design and implement a locally deployable intelligent guidance system for anesthesiology residency training. Methods: A knowledge graph-based intelligent guidance system was developed based on the 5A smart education framework. The system consists of four major components: (1) an anesthesia-specific four-layer knowledge graph ontology covering the complete perioperative workflow; (2) a progressive dialogue guidance mechanism enabling context-aware multi-turn interaction; (3) a dual-engine retrieval-augmented generation (RAG) framework integrating structured knowledge graph retrieval with semantic vector search; and (4) a lightweight fully local deployment architecture supporting offline operation. System…
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