检验医学 ›› 2025, Vol. 40 ›› Issue (5): 413-420.DOI: 10.3969/j.issn.1673-8640.2025.05.001

• 专家论坛 •    下一篇

AI技术重塑检验医学:从自动化到智能决策的跨越

龚晓霖1, 邓昆2, 伍均3, 任传利4, 许颂霄5, 李圣杰6, 李波7, 杨大干8, 沈瀚9, 张义10, 陈鸣11, 武永康12, 罗怀超13, 袁旭14, 徐华国15, 龚倩16, 李欣1(), 关明17()   

  1. 1.上海市临床检验中心《检验医学》编辑部,上海 200126
    2.重庆医科大学附属第三医院检验医学中心,重庆 401120
    3.上海交通大学医学院附属第一人民医院嘉定医院检验科,上海 201803
    4.江苏省苏北人民医院医学检验科,江苏 扬州 225001
    5.浙江省肿瘤医院检验科,浙江 杭州 310022
    6.复旦大学附属眼耳鼻喉科医院检验科,上海 200031
    7.重庆大学附属三峡医院检验科,重庆 404000
    8.浙江大学医学院附属第一医院检验科,浙江 杭州 310003
    9.南京大学医学院附属鼓楼医院检验科,江苏 南京 210008
    10.山东大学齐鲁医院检验医学中心,山东 济南 250012
    11.陆军军医大学第一附属医院(西南医院)检验科,重庆 400038
    12.金堂县第一人民医院(四川大学华西医院金堂医院),四川 金堂 610400
    13.四川省肿瘤医院·研究所 四川省肿瘤临床医学研究中心 四川省癌症防治中心 电子科技大学附属肿瘤医院检验科,四川 成都 610041
    14.华中科技大学同济医学院附属同济医院检验科,湖北 武汉 430030
    15.江苏省人民医院,江苏 南京 210029
    16.复旦大学附属中山医院青浦分院检验科,上海 201700
    17.复旦大学附属华山医院检验医学科,上海 200040
  • 收稿日期:2025-04-30 修回日期:2025-05-12 出版日期:2025-05-30 发布日期:2025-06-04
  • 通讯作者: 李 欣:E-mail:lixin7319@126.com;关 明:E-mail:guanming88@126.com
  • 作者简介:龚晓霖,男,1980年生,编辑,主要从事期刊编辑工作。
  • 基金资助:
    国家重点研发计划项目(2022YFC2406400)

AI technology reshaping laboratory medicine from automation to intelligent decision-making

GONG Xiaolin1, DENG Kun2, WU Jun3, REN Chuanli4, XU Songxiao5, LI Shengjie6, LI Bo7, YANG Dagan8, SHEN Han9, ZHANG Yi10, CHEN Ming11, WU Yongkang12, LUO Huaichao13, YUAN Xu14, XU Huaguo15, GONG Qian16, LI Xin1(), GUAN Ming17()   

  1. 1. Editorial Department of Laboratory Medicine,Shanghai Center for Clinical Laboratory,Shanghai 200126,China
    2. Center for Clinical Laboratory,the Third Affiliated Hospital of Chongqing Medical University,Chongqing 401120,China
    3. Department of Clinical Laboratory,Jiading Hospital,Shanghai General Branch of Shanghai Jiao Tong University School of Medicine,Shanghai 201803,China
    4. Department of Clinical Laboratory,Subei People's Hospital,Yangzhou 225001,Jiangsu,China
    5. Department of Clinical Laboratory,Zhejiang Cancer Hospital,Hanzhou 310022,Zhejiang,China
    6. Department of Clinical Laboratory,the Eye and Ear,Nose,Throat Hospital of Fudan University,Shanghai 200031,China
    7. Department of Clinical Laboratory,Chongqing University Three Gorges Hospital,Chongqing 404000,China
    8. Department of Clinical Laboratory,the First Affiliated Hospital of Zhejiang University School of Medicine,Hangzhou 310003,Zhejiang,China
    9. Department of Clinical Laboratory,Nanjing Drum Tower Hospital,Nanjing University Medical School,Nanjing 210008,Jiangsu,China
    10. Center for Clinical Laboratory,Qilu Hospital of Shandong University,Jinan 250012,Shandong,China
    11. Department of Clinical Laboratory,the First Affiliated Hospital(Southwest Hospital) of Army Medical University,Chongqing 400038,China
    12. Jintang First People's Hospital,West China Hospital (Jintang Hospital) of Sichuan University,Jintang 610400,Sichuan,China
    13. Department of Clinical Laboratory,Sichuan Clinical Research Center for Cancer,Sichuan Cancer Hospital & Institute,Sichuan Cancer Center,University of Electronic Science and Technology of China,Chengdu 610041,Sichuan,China
    14. Department of Clinical Laboratory,Tongji Hospital,Tongji Medical College of Huazhong University of Science and Technology,Wuhan 430030,Hubei,China
    15. Jiangsu Province Hospital,Nanjing 210029,Jiangsu,China
    16. Department of Clinical Laboratory,Qingpu Branch,Zhongshan Hospital,Fudan University,Shanghai 201700,China
    17. Department of Clinical Laboratory,Huashan Hospital,Fudan University,Shanghai 200040,China
  • Received:2025-04-30 Revised:2025-05-12 Online:2025-05-30 Published:2025-06-04

摘要:

在医疗行业加速数字化转型的背景下,人工智能(AI)技术正深刻改变医疗服务的模式。以 DeepSeek 为代表的开源大模型已逐步在全国各大医院落地,覆盖患者服务、临床诊疗、医院管理、科研支持等广泛的临床应用场景,构建出全流程智慧医疗生态。在检验医学领域,AI同样赋能于学科发展,在检验报告智能解读、检验流程优化、质量管理和科研创新中展现出实力和潜力。可以确定,AI 大模型必将推动检验医学从“数据生产”向“价值创造”转型,通过精准诊断助力个性化医疗和健康管理,提升医疗效能。然而,检验医学在受益于AI技术带来的红利的同时,也面临着数据质量、模型局限性和后续监管等一系列挑战。

关键词: 人工智能, 大模型, DeepSeek, 检验医学

Abstract:

Under accelerated digital transformation in medical industry,artificial intelligence(AI) technology is profoundly changing the mode of medical services. Open-source large models represented by DeepSeek have gradually been implemented in major hospitals across the country,covering a wide range of clinical application scenarios such as patient services,clinical diagnosis and treatment,hospital management and scientific research support,and constructing a full-process smart medical ecosystem. In the field of laboratory medicine,AI also empowers the development of the discipline,demonstrating its strength and potential in the intelligent interpretation of determination reports,the optimization of determination processes and scientific research innovation. AI large models will inevitably drive the transformation of laboratory medicine from datum production to value creation,assist personalized medicine and health management through accurate diagnosis,and enhance medical efficiency. While laboratory medicine benefits from the dividends brought by AI technology,it also faces a series of challenges such as datum quality,model limitations and subsequent supervision.

Key words: Artificial intelligence, Large model, DeepSeek, Laboratory medicine

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