Knowledge Extraction and Evaluation Framework for Medical Procedure Videos

Abstract

This study explores methods for knowledge extraction from medical procedure videos and the development of a comprehensive evaluation framework, with the aim of providing standardized and quantifiable analytical tools for medical education and clinical skills training. Leveraging advances in computer vision and natural language processing, a multimodal knowledge extraction framework is proposed that integrates action recognition, speech semantic analysis, and text annotation to enable the structured extraction of procedural workflows, critical steps, and error patterns. Furthermore, an evaluation system is established by combining hierarchical rating scales, the Behavioral Anchored Rating Scale (BARS) approach, and the Analytic Hierarchy Process (AHP). The framework incorporates weight allocation and reliability and validity testing to ensure objectivity and consistency in assessment outcomes. The proposed multimodal knowledge extraction and quantitative evaluation system can significantly enhance the efficiency and scientific rigor of medical procedure video analysis, providing valuable technical support for standardized skills training and distance medical education.