Purpose /Significance To construct a machine learning case library that is deeply integrated with large model and covers the full diagnosis and treatment process，so as to narrow the gap between clinical education and clinical application. Method /Process Based on real-world，de-identified multimodal medical data，in response to the teaching needs of the full diagnosis and treatment process，the case contents and practical tasks are designed around pre-examination，diagnosis，treatment，and follow-up，etc. Large models are used to support semantic parsing of medical records，multimodal fusion and process simulation，improving the efficiency and consistency of case organization，interactive clinical reasoning and teaching feedback. Result /Conclusion The proposed large model driven case library creates a simulated clinical environment，effectively enhances students’ clinical thinking and interdisciplinary innovation abilities，and provides a feasible solution for bridging the gap between machine learning education and real-world medical needs.