近年来，人工智能(AI)在皮肤病图像分析领域取得显著进展，其诊断敏感度与特异度已接近皮肤科专家水平，且能有效提升皮肤病鉴别能力。AI辅助教学也被证实可培养学员沟通能力，为AI应用皮肤病理教学奠定基础。作为连接临床与病理诊断的桥梁，传统皮肤病理教学主要以实时显微镜授课、教师现场讲解，以及病例探讨等方式展开，故而存在教学资源分配不均、优质标本不足、学习过程较为被动等情形。对军校学员而言，训练任务重、地域资源不均，教学内容与军事职业需求脱节，导致学习动机不足，极大限制了军校学员皮肤病理诊断能力的系统培养。AI技术通过全景数字切片、卷积神经网络及多模态模型等，实现切片自动识别标注、个性化学习路径推荐及虚拟现实沉浸式教学。具体应用于基础形态教学中自动标记、病例模拟训练整合多源信息，提供自适应测验与实时反馈及继续教育中构建终身学习平台，保障军校学员终身获取优质资源。但是当前阶段AI的应用在技术、教育实践、伦理与法律等领域仍存在诸多挑战，如对AI过度依赖、罕见病识别困难、数据隐私与安全风险等；未来要在强化AI技术研究的同时，注重军校学员教育理念的改革，使AI成为临床教学的主要辅助方式，推动军校学员皮肤病理教学朝着智能化、个性化和精准化的方向迈进。

In recent years, artificial intelligence (AI) has made significant progress in the field of dermatological image analysis. Its diagnostic sensitivity and specificity have approached the high level of dermatologists, and it can effectively enhance the ability to differentiate dermatological conditions. AI-assisted teaching has also been proven to cultivate trainees′ communication skills, laying the foundation for the application of AI in dermatopathology teaching. Dermatopathology serves as a bridge connecting clinical practice and pathological diagnosis. Traditional dermatopathology teaching is mainly conducted through microscopic slide review teaching, instructor-led demonstrations, and case discussions. Therefore, there are issues such as uneven distribution of teaching resources, insufficient high-quality specimens, and a relatively passive learning process. For military academy students, the burden of rigorous training, uneven regional resources, and the disconnect between the curriculum and military career requirements have led to insufficient learning motivation, severely limiting the systematic cultivation of their dermatopathological diagnostic abilities. AI technology leverages whole slide imaging, convolutional neural networks, and multimodal models to achieve automated slide recognition and annotation, personalized learning pathway recommendations, and virtual reality-based immersive instruction. Specifically, in basic morphology teaching, it automatically marks and integrates multi-source information for case simulation training, providing adaptive tests and real-time feedback, and building lifelong learning platforms in continuing education to ensure that military academy students maitain access to premium resources throughout their professional lives. However, currently the application of AI technology in the fields of technology, educational practice, ethics, and law still faces many challenges, such as excessive reliance on AI, difficulties in identifying rare diseases, data privacy and security risks, etc. Future efforts should focus not only on technological advancement but also on pedagogical reform, positioning AI as a primary adjunct in clinical teaching and driving military dermatopathology education toward intelligent, personalized, and precision-based paradigms.