From Research Frontier to Laboratory Bench: Design of a Four-Tier Experimental Teaching System for Multimodal Medical Image Intelligent Diagnosis
In plain terms
University programs teaching intelligent medical engineering are expanding, but their lab courses often don't keep up with the real-world complexities of AI in clinical settings, such as using multiple types of data (multimodal), handling rare cases (long-tailed), or adapting to changing data patterns (distributionally shifting). The researchers designed a new, four-level experimental teaching system. This system takes an ongoing research project on using advanced AI (multimodal deep learning) to diagnose endometrial cancer and turns it into a structured series of lab exercises for undergraduate students. They identified three key issues in current education (modality, authenticity, and deployment) and based their curriculum on established teaching frameworks like constructive alignment and experiential learning. The curriculum includes four progressive stages, plus an engineering layer, with 32 lab units spread over 64 hours. Students use a special virtual clinical workstation with real, but anonymized, multi-institutional patient data. Each stage focuses on a specific technical challenge, links to prior courses, and has clear assignments. They also outlined how data is handled, safety measures, and how students will be assessed. The plan is to collect data on student learning outcomes over two cycles to evaluate its effectiveness.