发表机构
Southwest Medical University; Wuhan University（西南医科大学; 武汉大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对智能医学工程本科实验课程滞后于临床AI现实的问题，本文提出将子宫内膜癌多模态深度学习研究转化为四层递进实验教学体系，含32个单元、64学时，并明确教学原则、技术映射及评估协议。
AI 中文摘要
智能医学工程本科专业正在扩展，然而实验室课程落后于临床AI的多模态、长尾和分布偏移的现实。本设计论文提出了一个先进的实验教学体系，将一项正在进行的关于子宫内膜癌的多模态深度学习研究项目转化为结构化的本科实验序列。我们识别了三个教育缺口（模态、真实性和部署），并从建构性对齐、体验式学习、研究教学联结和CDIO框架中推导出四个教学原则。课程包括四个递进层级外加一个工程层，共32个实验单元，64个接触学时，通过使用去标识化的多机构数据的定制虚拟临床工作站交付。每个层级对应特定的技术瓶颈、先修课程和标准参照的交付成果。数据治理、安全和评估协议均已明确。学习成果数据将在两个实施周期中收集。
英文摘要
Undergraduate programmes in intelligent medical engineering are expanding, yet laboratory curricula lag behind the multimodal, long-tailed, and distributionally shifting realities of clinical AI. This design paper presents an advanced experimental teaching system that translates an ongoing multimodal deep learning research project on endometrial carcinoma into a structured undergraduate lab sequence. We identify three educational gaps (modality, authenticity, and deployment) and derive four pedagogical principles from constructive alignment, experiential learning, the research teaching nexus, and the CDIO framework. The curriculum comprises four progressive tiers plus an engineering layer, with 32 laboratory units over 64 contact hours, delivered via a custom virtual clinical workstation using de-identified multi-institutional data. Each tier maps to a specific technical bottleneck, prerequisite coursework, and criterion-referenced deliverables. Data governance, safety, and assessment protocols are specified. Learning outcome data will be collected across two implementation cycles.
