病理形态学从数字化到智能化教学转型的构建

doi:10.16098/j.issn.0529-1356.2026.06.003

PDF(2364 KB)

解剖学报 ›› 2026, Vol. 57 ›› Issue (5) : 536-542. DOI: 10.16098/j.issn.0529-1356.2026.06.003

数智解剖教学专栏

病理形态学从数字化到智能化教学转型的构建

吴江华^1,2 王惠^1,2 尼华^1,2 田新霞^1,2* 张卫光^3*

作者信息 +

Construction of the digital-to-intelligent transformation in pathological morphology teaching

WU Jiang-hua^1,2, WANG Hui^1,2, NI Hua^1,2, TIAN Xin-xia^1,2*, ZHANG Wei-guang³*

Author information +

文章历史 +

摘要

数字病理技术突破了传统病理形态学教学资源的限制，但当前的数字化教学应用仍局限于切片图像的浏览，在自主学习支持、交互功能与评估反馈机制等方面仍未能满足以能力培养为核心的现代病理学形态学的教育要求。本文中我们结合医学教育智能化发展趋势，探讨将人工智能（AI）技术，尤其是将视觉-语言病理大模型与多模态知识图谱融入病理形态学的教学框架与实施路径，提出智慧病理形态教学平台的构建方案。该方案包括三维数字标本、AI切片分析，结构化多模态知识图谱及虚拟临床病例等主要模块。AI切片分析教学测试显示，AI辅助形态学教学可显著降低病理图像识别难度，学生使用需求与推广意愿均处于较高水平。本研究旨在推动病理实习教学在数字智能呈现，实时交互式学习及理论知识图谱关联等方面的创新，为病理形态学教育的智能化转型提供框架理论参考与实现路径支撑。

Abstract

Digital pathology technology has overcome the resource limitations inherent in traditional pathological morphology teaching. However, current digital teaching applications are still largely confined to slide image browsing, falling short in supporting self-directed learning, providing interactive functionalities, and establishing effective assessment and feedback mechanisms. Consequently, they fail to meet the competency-focused requirements of modern pathology morphology education. In line with the trend toward intelligent transformation in medical education, in this paper we explored the integration of artificial intelligence (AI) technologies-specifically vision-language pathology foundation models and multimodal knowledge graphs-into the teaching framework and implementation strategies for pathological morphology. We propose a blueprint for constructing an intelligent pathological morphology teaching platform. Its core modules comprise three-dimensional digital specimens, AI-powered slide analysis tools, structured multimodal knowledge graphs, and clinical cases. AI-assisted teaching tests show that AI markedly lowers the difficulty of pathological image recognition, with strong student demand and high promotion willingness. The platform is designed to drive innovation in pathology practical teaching by enhancing digital-intelligent visualization, enabling real-time interactive learning, and facilitating the correlation of theoretical knowledge. This work provides a theoretical framework and practical implementation pathways to support the intelligent transformation of pathological morphology education.

导出引用

吴江华王惠尼华田新霞张卫光. 病理形态学从数字化到智能化教学转型的构建[J]. 解剖学报. 2026, 57(5): 536-542 https://doi.org/10.16098/j.issn.0529-1356.2026.06.003

WU Jiang-hua, WANG Hui, NI Hua, TIAN Xin-xia, ZHANG Wei-guang. Construction of the digital-to-intelligent transformation in pathological morphology teaching[J]. Acta Anatomica Sinica. 2026, 57(5): 536-542 https://doi.org/10.16098/j.issn.0529-1356.2026.06.003

中图分类号： R361

参考文献

［1］Hanna MG, Ardon O, Reuter VE, et al. Integrating digital pathology into clinical practice［J］. Mod Pathol, 2022, 35(2):152-164.

［2］Ren LW, Chen M, Zhao FJ, et al. Application and survey analysis of the morphological digital platform in pathology experimental teaching ［J］. Education of Chinese Medicine, 2024, 43(5):129-132. (in Chinese)

任丽薇, 陈梦, 赵福建, 等. 形态学数字化平台在病理学实验教学中的应用与调查分析［J］. 中医教育, 2024, 43(5):129-132.

［3］Li J, Zhao CX, Ma LL. Application of digital section in pathology teaching reform ［J］. China Continuing Medical Education, 2023, 15(8):9-13. (in Chinese)

李姣, 赵彩霞, 马丽莉. 数字化切片在病理学教学改革中的应用［J］. 中国继续医学教育, 2023, 15(8):9-13.

［4］Wei XF, Wu JH, Chi XCh, et al. Exploring the path, challenges, and prospects of artificial intelligence in empowering human morphology curriculum construction ［J］. Chinese Journal of Medical Education, 2026, 46(1):8-14. (in Chinese)

魏潇凡, 吴江华, 迟晓春, 等. 人工智能赋能人体形态学课程建设的路径、挑战与前景［J］. 中华医学教育杂志, 2026, 46(1):8-14.

［5］Brodsky V, Ullah E, Bychkov A, et al. Generative artificial intelligence in anatomic pathology［J］. Arch Pathol Lab Med, 2025, 149(4):298-318.

［6］Bulten W, Kartasalo K, Chen PC, et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge［J］. Nat Med, 2022, 28(1):154-163.

［7］Shakthah LA, Carrero ZI, Hewitt KJ, et al. Application of artificial intelligence and digital tools in cancer pathology［J］. Lancet Digit Health, 2025, 7(10):e100933.

［8］Wu J, Liu C, Liu X, et al. Artificial intelligence-assisted system for precision diagnosis of PD-L1 expression in non-small cell lung cancer［J］. Mod Pathol, 2022, 35(3):403-411.

［9］Lu MY, Chen B, Williamson DFK, et al. A multimodal generative AI copilot for human pathology［J］. Nature, 2024, 634(8033):466-473.

［10］Omar M, Ullanat V, Loda M, et al. ChatGPT for digital pathology research［J］. Lancet Digit Health, 2024, 6(8):e595-e600.

［11］Ma J, He Y, Li F, et al. Segment anything in medical images［J］. Nat Commun, 2024, 15(1):654.

［12］Zeng WH, Wang LH, Pan RB, et al. Current situation and future challenges of anatomy teaching driven by artificial intelligence［J］. Basic Medical Education, 2025, 27(10):968-974. (in Chinese)

曾文泓, 王丽华, 潘荣斌, 等. 人工智能驱动下解剖学教学的现状与未来挑战［J］. 基础医学教育, 2025, 27(10):968-974.

［13］Liu GJ, Zhang ShX, Tan LW, et al. Application of digital human images and 3D models in anatomy teaching ［J］. Journal of Regional Anatomy and Operative Surgery, 2011, 20(2):210. (in Chinese)

刘光久, 张绍祥, 谭立文, 等. 数字化人体图像和三维模型在解剖教学中的应用［J］. 局解手术学杂志, 2011, 20(2):210.

［14］Song AH, Williams M, Williamson DFK, et al. Analysis of 3D pathology samples using weakly supervised AI［J］. Cell, 2024, 187(10):2502-2520.

［15］Wu XP, Li ZhH, Yang L, et al. Target teaching in human anatomy experiment teaching based on digital human anatomy system ［J］. Chinese Journal of Medical Education, 2019, 39(4):256-258. (in Chinese)

吴学平, 李志宏, 杨玲, 等. 借助“数字人”解剖系统在人体解剖学实验教学中实施目标教学的探讨［J］. 中华医学教育杂志, 2019, 39(4):256-258.

［16］Cheng QCh, Liu P, Liu HC, et al. Artificial intelligence-driven personalized teaching new paradigm for thoracic wall dissection ［J］, Acta Anatomica Sinica,2025,56(5):601-606. (in Chinese)

程全成,刘平,刘怀存,等. 人工智能驱动局部解剖学个性化教学新范式:以胸前壁局解为例［J］. 解剖学报,2025,56(5):601-606.

［17］Da Q, Wang ShH, Wang MW, et al. Progress and challenges of pathological artificial intelligence in the era of large models ［J］. Chinese Journal of Pathology, 2025, 54(3):305-309. (in Chinese)

笪倩, 王书浩, 王玟雯, 等. 大模型时代下的病理人工智能进展与挑战［J］. 中华病理学杂志, 2025, 54(3):305-309.

［18］Wahab N, Miligy IM, Dodd K, et al. Semantic annotation for computational pathology: multidisciplinary experience and best practice recommendations［J］. J Pathol Clin Res, 2022, 8(2):116-128.

［19］Hu X, Gu L, Kobayashi K, et al. Interpretable medical image Visual Question Answering via multi-modal relationship graph learning［J］. Med Image Anal, 2024, 97:103279.

［20］Yan JR, Deng WM, Zhang ShJ, et al. Study on knowledge graph empowering the training of clinical thinking ［J］. Chinese Journal of Medical Education, 2025, 45(12):900-905. (in Chinese)

闫景瑞, 邓为民, 张士杰, 等. 知识图谱赋能临床思维训练的研究［J］. 中华医学教育杂志, 2025, 45(12):900-905.

［21］Ni H, Wang H, Wu JH, et al. Exploration on an integrated mode in the practice teaching of pathology for undergraduate students of clinical medicine ［J］. Basic Medical Education, 2025, 27(11):1037-1040. (in Chinese)

尼华, 王惠, 吴江华, 等. 临床本科生病理实习课教学模式的探索与思考［J］. 基础医学教育, 2025, 27(11):1037-1040.

［22］Dobson ETA, Cimini B, Klemm AH, et al. ImageJ and CellProfiler: complements in open-source bioimage analysis［J］. Curr Protoc, 2021, 1(5):e89.

［23］Kumar M, Mani UA, Tripathi P, et al. Artificial hallucinations by google bard: think before you leap［J］. Cureus, 2023, 15(8):e43313.

［24］Hatem R, Simmons B, Thornton JE. A call to address ai “hallucinations” and how healthcare professionals can mitigate their risks［J］. Cureus, 2023, 15(9):e44720.

［25］Altorfer FCS, Kelly MJ, Avrumova F, et al. The double-edged sword of generative AI: surpassing an expert or a deceptive “false friend”［J］? Spine J, 2025, 25(8):1635-1643.

［26］He LL, Yang JY, Li L. Research on the digital literacy and innovation ability in pathology teachers under the background of digital transformation of education ［J］. Basic Medical Education, 2024, 26(9):783-787. (in Chinese)

何琳莉, 杨佳于, 李丽. 教育数字化背景下病理学教师数字素养及创新能力提升研究［J］. 基础医学教育, 2024, 26(9):783-787.