基于人工智能的解剖学CBL案例生成系统构建OA

The authors have developed an AI-based case generation system to address the challenges of difficult resource integration,time-consuming case design,and insufficient personalized support in the teaching of anatomy.They establish a local knowledge base by integrating medical imaging data and clinical guidelines,and also utilize a zero-code platform to build AI agents that can automati-cally link anatomical structures,radiographic features,and clinical information.The system can dynamically generate comprehensive cases combining patient history,anatomical knowledge,and clinical analysis to meet the teaching needs,while simultaneously support personalized interactive designs.Results show that the system significantly shortens design cycles while guaranteeing the accuracy of teaching content and it greatly enhances the efficiency of teaching preparation,providing practical reference for AI application to the teaching of anatomy.