PDF(733 KB)
PDF(733 KB)
数智病理平台构建及服务模式研究
Digital Intelligence Pathology Platform and Its Service Pattern
病理诊断是临床诊疗决策的核心依据，融合人工智能、大数据等信息技术构建的数智病理平台具有重大应用价值，将支持病理学科数字化、智慧化升级，拓展智慧病理中国方案。本文在明晰数智病理相关概念的基础上，梳理了数智病理的现实应用需求，识别出数智病理建设面临的直接挑战；立足自有研究进展及成果，从数据中台、算法中台、服务中台三方面出发，详细阐述了数智病理平台的系统架构；进一步分析了基于数智病理平台的病理科工作流程优化方向，涵盖病理样本标准化、病理诊断智能化、病理服务平台化等流程，进而前瞻了病理诊断、病理会诊、病理教学、病理科研、病理质量控制等数智病理平台服务模式应用场景。研究建议，明确政策导向、建立统一的行业标准，降低建设成本、探索多元化的资金渠道，壮大人才队伍、提高全行业的专业技术水平，推进技术革新、拓展病理服务能力，加强数据安全、防止患者隐私泄露，以加快数智病理应用、支撑智慧医疗革新。
Pathological diagnosis is the cornerstone for clinical diagnosis and treatment decision-making. The digital intelligence pathology platform built by integrating artificial intelligence, big data, and other information technologies has great application values, which will support the digitalization and intelligent upgrading of the pathology discipline and expand the Chinese solution of intelligent pathology. This study systematically clarifies the conceptual framework of digital intelligence pathology, identifies practical application requirements, and highlights critical challenges in its implementation. Building on proprietary research achievements, we propose a tripartite middleware architecture comprising data, algorithm, and service platforms. The system architecture integrates standardized data management, AI-driven analytical modules, and interoperable service interfaces to optimize pathological workflows. Key workflow improvements include standardized specimen processing, intelligent diagnostic assistance, and platform-based service integration. Furthermore, the study explores prospective application scenarios for digital intelligence pathology platforms, spanning diagnostic services, multidisciplinary consultations, medical education, scientific research, and quality control. Strategic recommendations are provided to accelerate adoption: establishing policy-guided industry standards, diversifying funding channels, strengthening professional training, advancing technological innovation, and ensuring data security with privacy protection. These measures aim to expedite the integration of digital intelligence pathology into clinical practice and support the evolution of smart healthcare.
数智病理 / 病理诊断 / 病理大模型 / 服务模式 / 病理学 / 病理科
digital intelligence pathology / pathological diagnosis / pathological foundation model / service pattern / pathology / pathology department
| [1] |
蛋壳研究院. 2023数字智慧病理行业研究报告 [R]. 重庆: 蛋壳研究院, 2023.
VCBeat Research. Research report on digital intelligence pathology industry (2023) [R]. Chongqing: VCBeat Research, 2023.
|
| [2] |
陈晓红, 许冠英, 徐雪松, 等. 我国算力服务体系构建及路径研究 [J]. 中国工程科学, 2023, 25(6): 49‒60.
Chen X H, Xu G Y, Xu X S, et al. Computing power service system of China and its development path [J]. Strategic Study of CAE, 2023, 25(6): 49‒60.
|
| [3] |
人工智能医疗器械创新合作平台, 中国信息通信研究院. 人工智能大模型赋能医疗健康产业白皮书(2023) [R].北京: 人工智能医疗器械创新合作平台, 中国信息通信研究院, 2023.
Artificial Intelligent Medical Device Innovation and Cooperation Platform, China Academy of Information and Communications Technology. White paper on empowering the healthcare industry with AI large languagemodels (2023) [R]. Beijing: Artificial Intelligent Medical Device Innovation and Cooperation Platform, China Academy of Information and Communications Technology, 2023.
|
| [4] |
陈晓红, 刘浏, 袁依格, 等. 医疗大模型技术及应用发展研究 [J]. 中国工程科学, 2024, 26(6): 77‒88.
Chen X H, Liu L, Yuan Y G, et al. Technology and application development of medical foundation model [J]. Strategic Study of CAE, 2024, 26(6): 77‒88.
|
| [5] |
Moor M, Banerjee O, Abad Z S H, et al. Foundation models for generalist medical artificial intelligence [J]. Nature, 2023, 616(7956): 259‒265.
|
| [6] |
Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge [J]. Nature, 2023, 620(7972): 172‒180.
|
| [7] |
Greenwald N F, Miller G, Moen E, et al. Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning [J]. Nature Biotechnology, 2022, 40(4): 555‒565.
|
| [8] |
Huang Z, Bianchi F, Yuksekgonul M, et al. A visual-language foundation model for pathology image analysis using medical Twitter [J]. Nature Medicine, 2023, 29(9): 2307‒2316.
|
| [9] |
余净纯, 郭明星, 韩靖, 等. 人工智能在病理诊断领域的进展 [J]. 分子影像学杂志, 2022, 45(5): 779‒789.
Yu J C, Guo M X, Han J, et al. Progress of artificial intelligence in the field of pathological diagnosis [J]. Journal of Molecular Imaging, 2022, 45(5): 779‒789.
|
| [10] |
Xu H W, Usuyama N, Bagga J, et al. A whole-slide foundation model for digital pathology from real-world data [J]. Nature, 2024, 630(8015): 181‒188.
|
| [11] |
Wang X Y, Zhao J H, Marostica E, et al. A pathology foundation model for cancer diagnosis and prognosis prediction [J]. Nature, 2024, 634(8035): 970‒978.
|
| [12] |
上海交通大学医学院附属瑞金医院. 数字化智慧病理科建设白皮书 [R]. 上海: 上海交通大学医学院附属瑞金医院, 2023.
Ruijin Hospital, School of Medcine of Shanghai Jiao Tong University. White paper being a digitalization and AI-driven pathology department 2023 [R]. Shanghai: Ruijin Hospital, School of Medcine of Shanghai Jiao Tong University, 2023.
|
| [13] |
步宏. 数字病理科和病理人工智能发展现状与展望 [J]. 临床与实验病理学杂志, 2023, 39(7): 769‒771.
Bu H. Development status and prospect of digital pathology department and pathological artificial intelligence [J]. Chinese Journal of Clinical and Experimental Pathology, 2023, 39(7): 769‒771.
|
| [14] |
杨善林, 丁帅, 顾东晓, 等. 医疗健康大数据驱动的知识发现与知识服务方法 [J]. 管理世界, 2022, 38(1): 219‒229.
Yang S L, Ding S, Gu D X, et al. Healthcare big data driven knowledge discovery and knowledge service approach [J]. Journal of Management World, 2022, 38(1): 219‒229.
|
| [15] |
李挺. 建设当代临床病理学科 [J]. 北京大学学报(医学版), 2023, 55(2): 197‒200.
Li T. Construction of contemporary clinical pathology discipline [J]. Journal of Peking University (Health Sciences), 2023, 55(2): 197‒200.
|
| [16] |
潘雅婷, 廖心怡, 于观贞. 人工智能在肿瘤病理诊断及临床决策中的展望 [J]. 第二军医大学学报, 2021, 42(10): 1168‒1174.
Pan Y T, Liao X Y, Yu G Z. Artificial intelligence in tumor pathological diagnosis and clinical decision-making: The prospect [J]. Academic Journal of Second Military Medical University, 2021, 42(10): 1168‒1174.
|
| [17] |
张卉, 李瑞玉, 梁智勇. 病理学向分子病理时代迈进 [J]. 中国科学: 生命科学, 2021, 51(8): 1101‒1106.
Zhang H, Li R Y, Liang Z Y. Molecular pathology: The new era of clinical pathology [J]. Scientia Sinica Vitae, 2021, 51(8): 1101‒1106.
|
| [18] |
魏瑞璇, 庞剑, 李少冬. 国内数字病理发展现状与展望 [J]. 中国医疗管理科学, 2023, 13(6): 60‒65.
Wei R X, Pang J, Li S D. Current development and prospect of digital pathology in China [J]. Chinese Journal of Medical Management Sciences, 2023, 13(6): 60‒65.
|
| [19] |
姚建国, 叶楠, 余力. 数字病理流程中需要注意的一些技术问题 [J]. 临床与实验病理学杂志, 2024, 40(8): 802‒806.
Yao J G, Ye N, Yu L. Some technical problems needing attention in digital pathology process [J]. Chinese Journal of Clinical and Experimental Pathology, 2024, 40(8): 802‒806.
|
| [20] |
Shmatko A, Ghaffari Laleh N, Gerstung M, et al. Artificial intelligence in histopathology: Enhancing cancer research and clinical oncology [J]. Nature Cancer, 2022, 3(9): 1026‒1038.
|
| [21] |
Wang W W, Zhao Y S, Teng L H, et al. Neuropathologist-level integrated classification of adult-type diffuse gliomas using deep learning from whole-slide pathological images [J]. Nature Communications, 2023, 14(1): 6359.
|
| [22] |
Wu Z Q, Trevino A E, Wu E, et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens [J]. Nature Biomedical Engineering, 2022, 6(12): 1435‒1448.
|
| [23] |
Yoon C, Park E, Misra S, et al. Deep learning-based virtual staining, segmentation, and classification in label-free photoacoustic histology of human specimens [J]. Light: Science & Applications, 2024, 13(1): 226.
|
| [24] |
Nahhas O S M, Loeffler C M L, Carrero Z I, et al. Regression-based deep-learning predicts molecular biomarkers from pathology slides [J]. Nature Communications, 2024, 15: 1253.
|
| [25] |
姚建国. 数字病理临床应用现状及前景展望 [J]. 四川大学学报(医学版), 2021, 52(2): 156‒161.
Yao J G. Digital pathology: Current status and prospects of clinical application [J]. Journal of Sichuan University (Medical Sciences), 2021, 52(2): 156‒161.
|
| [26] |
平轶芳, 闫红, 卞修武. 人工智能病理在肿瘤精准医疗时代的应用与挑战 [J]. 生命科学, 2022, 34(8): 929‒940.
Ping Y F, Yan H, Bian X W. Artificial intelligence pathology in the age of oncology precision medicine: Applications and challenges [J]. Chinese Bulletin of Life Sciences, 2022, 34(8): 929‒940.
|
| [27] |
Montezuma D, Oliveira S P, Neto P C, et al. Annotating for artificial intelligence applications in digital pathology: A practical guide for pathologists and researchers [J]. Modern Pathology, 2023, 36(4): 100086.
|
| [28] |
丁维龙, 刘津龙, 朱伟, 等. 基于深度学习的病理切片质量控制算法综述 [J]. 计算机科学, 2024, 51(10): 276‒286.
Ding W L, Liu J L, Zhu W, et al. Review of quality control algorithms for pathological slides based on deep learning [J]. Computer Science, 2024, 51(10): 276‒286.
|
| [29] |
Lu M Y, Chen B W, Williamson D F K, et al. A visual-language foundation model for computational pathology [J]. Nature Medicine, 2024, 30(3): 863‒874.
|
| [30] |
Chen R J, Ding T, Lu M Y, et al. Towards a general-purpose foundation model for computational pathology [J]. Nature Medicine, 2024, 30(3): 850‒862.
|
/
| 〈 |
|
〉 |
