人工智能视野下病理学创新教学的发展研究

Research on the Development of Innovative Pathology Teaching from the Perspective of Artificial Intelligence

摘要: 人工智能(Artificial Intelligence, AI)正成为病理学教学改革的重要驱动力。本文梳理了数字病理与虚拟显微(WSI/VM)、虚拟现实与增强现实(VR/AR)、计算病理与可解释人工智能(XAI)、大语言模型(LLMs)等在病理学教育中的应用进展及相关质量与合规要求，指出其在突破玻片与课堂时空限制、提升形态学识别能力和临床思维、支撑个性化学习与形成性评价等方面的优势。在此基础上，提出以“人工智能 + 病理学”为核心的分层课程体系，倡导将VR/AR与虚拟显微深度融入课堂教学，构建过程性、能力性与创新性相结合的多元评价机制，并组建跨学科协同教学团队，推动教学范式由“知识传授”向“能力培养”转变。与此同时，本文分析了技术适配、资源不均衡、伦理与隐私保护以及教师角色转变等现实挑战，认为需在政策引导、资源共享与师资培训等方面持续发力，促进人工智能在病理学教学中的规范化与深度融合。

Abstract: Artificial intelligence (AI) is becoming a key driver of innovation in pathology education. This article reviews the current applications of digital pathology and virtual microscopy, immersive virtual and augmented reality (VR/AR), computational pathology with explainable AI, and large language models (LLMs) in pathology teaching, together with emerging requirements for data security and quality assurance. These technologies help overcome constraints related to slide resources, time and space, enhance students’ abilities in morphological recognition and mechanism understanding, and support personalized learning as well as formative assessment. Building on this, we propose an AI-enhanced pathology curriculum framework that integrates basic, advanced and extended training modules, promotes the deep integration of VR/AR and virtual microscopy into classroom teaching, and develops a multidimensional assessment system combining process-, competence- and innovation-oriented evaluations. We further advocate the construction of interdisciplinary teaching teams involving experts in pathology, artificial intelligence, computer science and education to foster collaborative curriculum design. At the same time, the article highlights major challenges, including mismatches between educational needs and existing clinical AI tools, unequal access to digital infrastructure, ethical and privacy concerns, and the transformation of teachers’ roles. Continuous efforts in policy support, resource sharing and faculty development are required to achieve responsible and in-depth integration of AI into pathology education and to facilitate a shift from knowledge transmission to competency-based training.