人工智能在血细胞分析复检流程中的研究进展与应用展望

doi:10.3877/cma.j.issn.2095-5820.2026.03.005

中华临床实验室管理电子杂志 ›› 2026, Vol. 14 ›› Issue (03) : 217 -226. doi: 10.3877/cma.j.issn.2095-5820.2026.03.005

综述

人工智能在血细胞分析复检流程中的研究进展与应用展望

何永建¹, 吴新忠², 杭建峰³, 高雅¹, 安泰学¹, 王玉亭⁴, 孙德华¹^,^†(

)

¹　510515　广东广州，南方医科大学南方医院检验医学科，广东省单细胞技术与应用重点实验室
²　510120　广东广州，广东省中医院检验科
³　510010　广东广州，中国人民解放军南部战区总医院检验科
⁴　518106　广东深圳，深圳市帝迈生物技术有限公司AI检验事业部

收稿日期:2026-06-11 出版日期:2026-08-28
通信作者: 孙德华
基金资助:
2023年度广东省基础与应用基础研究基金企业联合基金(2023A1515220180); 南方医科大学南方医院院长基金(2023A035)

Research progress and application prospects of artificial intelligence in hematology analyzers re-examination workflow

Yongjian He¹, Xinzhong Wu², Jianfeng Hang³, Ya Gao¹, Taixue An¹, Yuting Wang⁴, Dehua Sun¹^,^†()

¹ Department of Laboratory Medicine, Nanfang Hospital, Southern Medical University, Guangdong Provincial Key Laboratory of Single Cell Technology and Application, Guangzhou Guangdong 510515, China
² Department of Clinical Laboratory, Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou Guangdong 510120, China
³ Department of Clinical Laboratory, General Hospital of Southern Theater Command of the Chinese People's Liberation Army, Guangzhou Guangdong 510010, China
⁴ AI Laboratory Division, Shenzhen Dymind Biotechnology Co., Ltd., Shenzhen Guangdong 518106, China

Received:2026-06-11 Published:2026-08-28
Corresponding author: Dehua Sun

全文 ( ) 下载PDF ( ) 导出引用

引用本文：

何永建, 吴新忠, 杭建峰, 高雅, 安泰学, 王玉亭, 孙德华. 人工智能在血细胞分析复检流程中的研究进展与应用展望[J/OL]. 中华临床实验室管理电子杂志, 2026, 14(03): 217-226.

Yongjian He, Xinzhong Wu, Jianfeng Hang, Ya Gao, Taixue An, Yuting Wang, Dehua Sun. Research progress and application prospects of artificial intelligence in hematology analyzers re-examination workflow[J/OL]. Chinese Journal of Clinical Laboratory Management(Electronic Edition), 2026, 14(03): 217-226.

血细胞分析仪可提供全血细胞计数、白细胞分类计数、白细胞群落参数（CPD）等数值结果，以及散点图、直方图等图形数据，形成多维度检测体系，是临床最基础、应用最广泛的疾病筛查工具。然而，以细胞计数及仪器报警为基础的血细胞分析通用复检规则难以匹配不同医疗机构的临床标本特征，漏检风险高，且复检负荷大。近年来，人工智能（AI）在医学图像识别领域取得突破性进展，为图形数据的智能分析提供了新路径。本文系统梳理了血细胞分析仪多维度信号特征与传统复检规则融合的可行性，将AI复检流程的发展划分为传统规则应用、特征参数建模、图形数据智能分析、多模态数据融合4个阶段；重点阐述了基于CPD的机器学习筛查方法、深度学习图形分析的技术优势及多模态融合的临床应用效果，并分析了高质量标准数据集稀缺、算法“黑箱”、评价体系缺失等核心挑战。未来可构建数值、图像与临床信息一体化的智能复检流程审核体系，通过人机协同提升异常标本检出能力，减少无效复检。

关键词: 血细胞分析, 人工智能, 复检流程, 白细胞群落参数, 多模态融合

Hematology analyzers serve as the most fundamental and widely used disease screening tool in clinical practice, offering a multidimensional detection system that provides numerical results such as complete blood count, white blood cell differential count and cell population data (CPD), alongside graphical data including scattergrams and histograms. However, universal review criteria based on cell counts and instrument flags fail to adapt to the specimen characteristics of different medical institutions, thus resulting in high false-negative risks and a heavy review burden. In recent years, artificial intelligence (AI) has achieved breakthrough advances in medical image recognition, providing a novel approach for the intelligent analysis of hematological graphical data. This paper systematically examines the feasibility of integrating multidimensional signal features from hematology analyzers with traditional review criteria, and divides the development of AI-assisted re-examination workflow into 4 stages: Traditional rule implementation, feature-parameter modeling, intelligent graphical data analysis, and multimodal data fusion. It elaborates on CPD-based machine learning screening methods, the strengths of deep learning in graphical analysis, and the clinical efficacy of multimodal fusion, while analyzing core challenges including the scarcity of high-quality datasets, the algorithmic "black box" issue, and the lack of standardized evaluation frameworks. Looking ahead, the construction of an integrated intelligent review system incorporating numerical parameters, graphical features and clinical information will enhance the detection capacity for abnormal specimens via human-machine collaboration, thereby reducing unnecessary manual reviews.

Key words: hematology analyzers, artificial intelligence, re-examination workflow, cell population data, multi-modal fusion

图1 基于AI辅助的血细胞分析智能复检流程

图2 血细胞分析和外周血形态镜检对比

图3 人工智能（AI）技术在血细胞分析复检流程中的应用与发展

表1 人工智能（AI）在血细胞分析复检流程中的四阶段发展概览

阶段	数据输入	主要方法	优势	局限
基于固定规则的逻辑判断	CBC、白细胞分类计数、仪器异常报警等。	固定阈值、决策树、评分系统，包括国际和本土化复检规则。	规则清晰、易解释、易部署。	阈值固定，难适应不同疾病谱和复杂异常样本；存在漏检或复检率升高风险。
基于限定规则的机器学习方法	CBC、白细胞分类计数、CPD等数值数据。	人工特征工程 + 机器学习，如Logistic回归、随机森林、XGBoost、LightGBM、人工神经网络。	数据结构化、模型轻量、成本低，易嵌入现有流程。	依赖人工特征；CPD 难以完整反映图形空间分布；跨仪器泛化受限。
基于图像模式的深度学习方法	散点图、直方图等图形数据。	采用深度学习方法直接解析图像模式。	可自动提取空间分布、局部异常和弱异常信号。	依赖高质量标注图像；罕见病和弱特征样本泛化能力仍需验证；可解释性不足。
基于多模态融合的AI方法	数值数据、图形数据、仪器报警、临床信息等多模态数据。	融合数值参数、图形特征和临床信息；规则与AI算法联合决策。	兼顾数值稳定性、图形敏感性和临床特异性，可降低复检负担。	数据接口、标准化、隐私合规、责任边界和临床解释规范仍不完善。

表1 人工智能（AI）在血细胞分析复检流程中的四阶段发展概览

阶段	数据输入	主要方法	优势	局限
基于固定规则的逻辑判断	CBC、白细胞分类计数、仪器异常报警等。	固定阈值、决策树、评分系统，包括国际和本土化复检规则。	规则清晰、易解释、易部署。	阈值固定，难适应不同疾病谱和复杂异常样本；存在漏检或复检率升高风险。
基于限定规则的机器学习方法	CBC、白细胞分类计数、CPD等数值数据。	人工特征工程 + 机器学习，如Logistic回归、随机森林、XGBoost、LightGBM、人工神经网络。	数据结构化、模型轻量、成本低，易嵌入现有流程。	依赖人工特征；CPD 难以完整反映图形空间分布；跨仪器泛化受限。
基于图像模式的深度学习方法	散点图、直方图等图形数据。	采用深度学习方法直接解析图像模式。	可自动提取空间分布、局部异常和弱异常信号。	依赖高质量标注图像；罕见病和弱特征样本泛化能力仍需验证；可解释性不足。
基于多模态融合的AI方法	数值数据、图形数据、仪器报警、临床信息等多模态数据。	融合数值参数、图形特征和临床信息；规则与AI算法联合决策。	兼顾数值稳定性、图形敏感性和临床特异性，可降低复检负担。	数据接口、标准化、隐私合规、责任边界和临床解释规范仍不完善。

1	JACOB E A. Complete blood cell count and peripheral blood film, its significant in laboratory medicine: A review study[J]. American journal of laboratory medicine, 2016, 1(3): 34-57.
2	SEO I H, LEE Y J. Usefulness of complete blood count (CBC) to assess cardiovascular and metabolic diseases in clinical settings: A comprehensive literature review[J]. Biomedicines, 2022, 10(11): 2697.
3	AMBAYYA A, SATHAR J, HASSAN R. Neoteric algorithm using cell population data (VCS parameters) as a rapid screening tool for haematological disorders[J]. Diagnostics (Basel), 2021, 11(9): 1652.
4	HAIDER R Z, UJJAN I U, SHAMSI T S. Cell population data-driven acute promyelocytic leukemia flagging through artificial neural network predictive modeling[J]. Translational oncology, 2020, 13(1): 11-16.
5	MISHRA S, CHHABRA G, PADHI S, et al. Usefulness of leucocyte cell population data by sysmex XN1000 hematology analyzer in rapid identification of acute leukemia[J]. Indian journal of hematology & blood transfusion: An official journal of Indian Society of Hematology and Blood Transfusion, 2022, 38(3): 499-507.
6	NINGOMBAM A, ACHARYA S, SARKAR A, et al. Scattergram patterns of hematological malignancies on sysmex XN-series analyzer[J]. Journal of applied hematology, 2021, 12(2): 83-89.
7	SACCHETTI S, BELLIA M, ZANOTTI V, et al. Detection of a lymphoproliferative disorder with suspected scattergram analysis using the mindray BC-6800 plus automated hematology analyzer: A case report[J]. International journal of laboratory hematology, 2025, 47(3): 373-375.
8	CAI Q, YE B, ZHENG W, et al. Development of a screening model for APL using cell population data and deep learning-extracted WBC scattergram features[J]. BMC cancer, 2025, 25(1): 1725.
9	李文利, 梁肖云, 皮蕾, 等. 血常规智能审核复检规则的建立与应用[J]. 广州医药, 2020, 51(3): 126-129.
10	WANG X, WANG X, GE P, et al. Establishment of improved review criteria for hematology analyzers in cancer hospitals[J]. Journal of clinical laboratory analysis, 2021, 35(2): e23638.
11	COMAR S R, MALVEZZI M, PASQUINI R. Are the review criteria for automated complete blood counts of the International Society of Laboratory Hematology suitable for all hematology laboratories?[J]. Revista brasileira de hematologia e hemoterapia, 2014, 36(3): 219-225.
12	LIU Y, JAIN A, ENG C, et al. A deep learning system for differential diagnosis of skin diseases[J]. Nature medicine, 2020, 26(6): 900-908.
13	DE FAUW J, LEDSAM J R, ROMERA-PAREDES B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease[J]. Nature medicine, 2018, 24(9): 1342-1350.
14	ESTEVA A, KUPREL B, NOVOA R A, et al. Dermatologist-level classification of skin cancer with deep neural networks[J]. Nature, 2017, 542(7639): 115-118.
15	LIAO H, XU Y, MENG Q, et al. A convolutional neural network-based, quantitative complete blood count scattergram-mapping framework promptly screens acute promyelocytic leukemia with high sensitivity[J]. Cancer, 2023, 129(19): 2986-2998.
16	中华人民共和国国家卫生健康委员会. 临床实验室定量检验结果的自动审核: WS/T 616-2018[S/OL]. (2018-08-20)[2026-06-12].
17	中国医学装备协会检验医学分会. 临床实验室检验结果自动审核程序建立及应用专家共识(2025版)[J]. 中华检验医学杂志, 2025, 48(10): 1291-1301.
18	ROLAND K, YAKIMEC J, MARKIN T, et al. Customized middleware experience in a tertiary care hospital hematology laboratory[J]. Journal of pathology informatics, 2022, 13: 100143.
19	孙士鹏, 刘贵建. 人工智能辅助外周血细胞形态学检验带来的机遇和期待[J]. 中华医学杂志, 2024, 104(33): 3087-3091.
20	LIAO H, ZHANG F, CHEN F, et al. Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects[J]. Acta pharmaceutica Sinica B, 2025, 15(11): 5702-5733.
21	NAZHA A, ELEMENTO O, AHUJA S, et al. Artificial intelligence in hematology[J]. Blood, 2025, 146(19): 2283-2292.
22	GREEN R, WACHSMANN-HOGIU S. Development, history, and future of automated cell counters[J]. Clinics in laboratory medicine, 2015, 35(1): 1-10.
23	COURVILLE E L, GRANT M, MASON E, et al. Performance of automated hematology analyzer criteria in detecting peripheral blood smear abnormalities: A systematic literature review[J]. International journal of laboratory hematology, 2026.
24	GULATI G, UPPAL G, GONG J. Unreliable automated complete blood count results: Causes, recognition, and resolution[J]. Annals of laboratory medicine, 2022, 42(5): 515-530.
25	HOYER J D, FISHER C P, SOPPA V M, et al. Detection and classification of acute leukemia by the Coulter STKS Hematology Analyzer[J]. American journal of clinical pathology, 1996, 106(3): 352-358.
26	薛桂阳, 曹岩, 刘明开. Sysmex XN-1000自动血细胞分析仪复检规则的建立与评估[J]. 实用检验医师杂志, 2019, 11(3): 154-157.
27	黎晓绮, 郭翼华, 陈世友, 等. 国外血细胞分析仪复检规则实际应用的探讨[J]. 临床血液学杂志(输血与检验版), 2015, 28(1): 105-106, 109.
28	宋金萍, 黄睿, 卢佩佩. BC-6800血细胞分析仪白细胞分类复检规则的制定及应用评价[J]. 海南医学, 2016, 27(23): 3831-3833.
29	寿玮龄, 任爱武, 王庚, 等. 血液分析仪散点图提示疟原虫感染2例报道[J]. 检验医学, 2019, 34(7): 672-674.
30	REHAN M, KHALID A, NASREEN F. White blood cell differential fluorescence abnormal scattergram: A useful indicator for early detection of malarial parasite[J]. Pakistan journal of medical sciences, 2022, 38(3Part-I): 687-691.
31	赵天赐, 李建英, 连荷清, 等. 白细胞散点图识别模型的建立与验证[J]. 临床检验杂志, 2022, 40(4): 246-250.
32	孙胜利, 李建英, 连荷清, 等. 基于对比学习的急性早幼粒细胞白血病智能检测算法模型在全血细胞分析中的建立与验证[J]. 临床检验杂志, 2024, 42(4): 252-255.
33	FANG K, CHEN X, DONG Z, et al. Developing and validating a highly sensitive platelet clump detection model for the sysmex haematology analyser[J]. Annals of clinical biochemistry, 2023, 60(2): 126-135.
34	敬一佩, 李建英, 郭野, 等. 白细胞散点图对急性早幼粒细胞白血病辅助诊断的价值探讨[J]. 标记免疫分析与临床, 2025, 32(5): 981-987.
35	LITJENS G, KOOI T, BEJNORDI B E, et al. A survey on deep learning in medical image analysis[J]. Medical image analysis, 2017, 42: 60-88.
36	SHEN D, WU G, SUK H I. Deep learning in medical image analysis[J]. Annual review of biomedical engineering, 2017, 19(1): 221-248.
37	BARNES P W, MCFADDEN S L, MACHIN S J, et al. The international consensus group for hematology review: Suggested criteria for action following automated CBC and WBC differential analysis[J]. Laboratory hematology: Official publication of the International Society for Laboratory Hematology, 2005, 11(2): 83-90.
38	中华医学会检验分会全国血液学复检专家小组, 中华检验医学杂志编辑委员会. 全国血液学复检专家小组工作会议纪要暨血细胞自动计数复检标准释义[J]. 中华检验医学杂志, 2007, 30(4): 380-382.
39	丛玉隆, 王昌富, 乐家新. 血细胞自动化分析后血涂片复审标准制定的原则与步骤[J]. 中华检验医学杂志, 2008, 31(7): 729-732.
40	王洁, 许东雯, 邵伟军, 等. 全自动血液分析流水线血片复检规则的设置及临床应用探讨[J]. 检验医学, 2008, 23(6): 558-562.
41	WINTHER-LARSEN A, VESTERGAARD E M, ABILDGAARD A. Clinical value of smear review of flagged samples analyzed with the Sysmex XN hematology analyzer[J]. Clinical chemistry and laboratory medicine, 2025, 63(3): 636-644.
42	AMBAYYA A, SAHIBON S, YANG T W, et al. A novel algorithm using cell population data (VCS parameters) as a screening discriminant between alpha and beta thalassemia traits[J]. Diagnostics (Basel), 2021, 11(11): 2163.
43	刘婷婷. 异常白细胞分类计数散点图对外周血涂片镜检的价值评估[J]. 中国现代医药杂志, 2024, 26(4): 89-93.
44	BURACK W R, LICHTMAN M A. The complete blood count-time to assess what is impactful and what is distracting[J]. JAMA network open, 2025, 8(6): e2514055.
45	GO L T, GO L T, GUNARATNE M D S K, et al. Variation in complete blood count reports across US hospitals[J]. JAMA network open, 2025, 8(6): e2514050.
46	SYED-ABDUL S, FIRDANI R P, CHUNG H J, et al. Artificial intelligence based models for screening of hematologic malignancies using cell population data[J]. Scientific reports, 2020, 10(1): 4583.
47	ALCAZER V, LE MEUR G, ROCCON M, et al. Evaluation of a machine-learning model based on laboratory parameters for the prediction of acute leukaemia subtypes: A multicentre model development and validation study in France[J]. The Lancet. Digital health, 2024, 6(5): e323-e333.
48	XING Y, LIU X, DAI J, et al. Artificial intelligence of digital morphology analyzers improves the efficiency of manual leukocyte differentiation of peripheral blood[J]. BMC medical informatics and decision making, 2023, 23(1): 50.
49	IWATA H, SHIBAYAMA T, WATANABE M, et al. Toward clinical reliability: Visualizing and interpreting AI-based classification in peripheral blood smear analysis[J]. Machine learning with applications, 2025, 22(c): 100780.
50	VIRK H, VARMA N, NASEEM S, et al. Utility of cell population data (VCS parameters) as a rapid screening tool for acute myeloid leukemia (AML) in resource-constrained laboratories[J]. Journal of clinical laboratory analysis, 2019, 33(2): e22679.
51	CHELI E, CHEVALIER S, KOSMIDER O, et al. Diagnosis of acute promyelocytic leukemia based on routine biological parameters using machine learning[J]. Haematologica, 2022, 107(6): 1466-1469.
52	常楠, 魏亚丽, 路其凤, 等. 基于血液分析仪参数构建急性早幼粒细胞白血病机器学习预警模型[J]. 检验医学, 2025, 40: 1190-1196.
53	LAI J X, TANG J W, GONG S S, et al. Development and validation of an interpretable risk prediction model for the early classification of thalassemia[J]. NPJ digital medicine, 2025, 8(1): 346.
54	AGUIRRE U, URRECHAGA E. Diagnostic performance of machine learning models using cell population data for the detection of sepsis: A comparative study[J]. Clinical chemistry and laboratory medicine, 2023, 61(2): 356-365.
55	URRECHAGA E, BÓVEDA O, AGUIRRE U. Improvement in detecting sepsis using leukocyte cell population data (CPD)[J]. Clinical chemistry and laboratory medicine, 2019, 57(6): 918-926.
56	MIYAJIMA Y, NIIMI H, UENO T, et al. Predictive value of cell population data with Sysmex XN-series hematology analyzer for culture-proven bacteremia[J]. Frontiers in medicine (Lausanne), 2023, 10: 1156889.
57	WICKSTRØM K E, HOLTEN A R, PREBENSEN C, et al. Sysmex cell population data for diagnosing infection in patients with suspected sepsis in the emergency department[J]. International journal of laboratory hematology, 2026, 48(1): 71-80.
58	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
59	张恩铭, 杨超, 陈先春, 等. 基于深度学习的血小板图形复检新策略的研究与应用[J]. 中华检验医学杂志, 2025, 48(9): 1201-1206.
60	TUNCER S A, AYYILDIZ H, KALAYCI M, et al. Scat-NET: COVID-19 diagnosis with a CNN model using scattergram images[J]. Computers in biology and medicine, 2021, 135: 104579.
61	PLEBANI M, BUTTARELLO M, RANIERI S C, et al. The complete blood count: Data or clinical information?[J]. Clinical chemistry and laboratory medicine, 2026.
62	FOY B H, PETHERBRIDGE R, ROTH M T, et al. Haematological setpoints are a stable and patient-specific deep phenotype[J]. Nature, 2025, 637(8045): 430-438.
63	BURACK W R, LICHTMAN M A. The complete blood count: increasing its precision and impact[J]. Annals of internal medicine, 2023, 176(8): eL230165.
64	POZDNYAKOVA O, YAEGER L H, SHIRAI C L, et al. Practical considerations in the implementation of peripheral smear review in the clinical laboratory[J]. International journal of laboratory hematology, 2026.
65	RIMAC V, VLAŠIĆ TANASKOVIĆ J, JOKIĆ A, et al. National recommendations of the working group for post-analytics of the Croatian Society of Medical Biochemistry and Laboratory Medicine: Implementation of autovalidation procedures[J]. Biochemia medica, 2025, 35(1): 010503.
66	STARKS R D, MERRILL A E, DAVIS S R, et al. Use of middleware data to dissect and optimize hematology autoverification[J]. Journal of pathology informatics, 2021, 12(1): 19.
67	WANG Y, ZHU G, CUI L, et al. Generative AI revolutionizes hematology analysis: Empowering laboratory and clinical diagnostics[J]. International journal of laboratory hematology, 2026, 48(S1): 4-96.
68	周晓旻, 林素美, 施勇纶, 等. 修改WAM-H Standard Rule警示系統规则可降低临床人工阅片的比率[J]. 生物医学暨检验科学杂志, 2011, 23(2): 46-52.
69	CIEPIELA O, KOTULA I, KIERAT S, et al. A comparison of Mindray BC-6800, Sysmex XN-2000, and Beckman Coulter LH750 automated hematology analyzers: A pediatric study[J]. Journal of clinical laboratory analysis, 2016, 30(6): 1128-1134.
70	CLINICAL AND LABORATORY STANDARDS INSTITUTE. Measurement procedure comparison and bias estimation using patient samples: CLSI EP09[S]. Wayne, PA: Clinical and Laboratory Standards Institute, 2018.
71	YAN W, HUANG L, XIA L, et al. MRI manufacturer shift and adaptation: Increasing the generalizability of deep learning segmentation for MR images acquired with different scanners[J]. Radiology. Artificial intelligence, 2020, 2(4): e190195.
72	中国人民共和国国家卫生健康委员会. 临床检验结果互认的基本技术条件及质量指标: WS/T 885-2026[S/OL]. (2026-05-25)[2026-06-11].

[1]	陈诺, 罗细顺, 刘福, 章敏, 成晨, 赵胜. 人工智能质控平台在提高中孕期系统性超声检查图像质量中的应用价值[J/OL]. 中华医学超声杂志（电子版）, 2026, 23(03): 228-235.
[2]	王燕妮. AI在创面管理中的潜力与挑战[J/OL]. 中华损伤与修复杂志(电子版), 2026, 21(04): 320-320.
[3]	孟凡征, 王阳清, 张珅瑜, 刘连新. 人工智能在肝癌微创外科中的发展[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 307-310.
[4]	王葵, 林宇轩, 沈皓, 沈锋. 人工智能化在肝癌微创外科的实践[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 311-314.
[5]	王鑫, 阎龙, 戴朝六. 人工智能化与个体化在肝癌微创外科的应用[J/OL]. 中华普外科手术学杂志(电子版), 2026, 20(04): 315-318.
[6]	李佳颖, 刘溪, 陈宇航, 桂程鹏, 习明, 陈炜, 罗俊航. 卷积神经网络在预测肾透明细胞癌全视野数字切片肿瘤坏死中的应用研究[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(04): 389-398.
[7]	黄楚曦, 吴卓. 人工智能在膀胱癌影像中的应用进展[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(03): 241-247.
[8]	郑瑞, 何天霖. 人工智能引领胃癌外科治疗迈向精准微创新时代[J/OL]. 中华腔镜外科杂志(电子版), 2026, 19(03): 183-186.
[9]	冯源康, 薄智豪, 王勇. 机器人外科的演进与未来[J/OL]. 中华腔镜外科杂志(电子版), 2026, 19(03): 187-192.
[10]	石亚超, 魏六木, 东小鸽, 樊海宁, 侯立朝, 杜凯豪, 汪占金, 薛伟伟, 刘海刚, 王展. 肝癌多模式诊断研究进展与展望[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 326-336.
[11]	许润怡, 杨正, 唐冲, 冯珍, 关振鹏, 李晓. 医学专科领域大语言模型的应用与发展[J/OL]. 中华临床医师杂志(电子版), 2026, 20(05): 361-373.
[12]	孙雪梅, 赵海丹. 基于随机对照试验的人工智能导诊系统对三甲医院门诊服务质量的影响研究[J/OL]. 中华临床医师杂志(电子版), 2026, 20(03): 222-227.
[13]	胡伟铭, 陈为谦, 林桂涵, 陈炜越, 胡展宁, 马成, 纪建松. 人工智能在冠心病介入治疗及预后评估中的应用[J/OL]. 中华介入放射学电子杂志, 2026, 14(03): 243-249.
[14]	曲林琳, 方蕾, 彭明婷. 《血细胞分析仪室内质量控制策略ICSH指南》解读[J/OL]. 中华临床实验室管理电子杂志, 2026, 14(03): 183-189.
[15]	任天瑞, 杨红柳, 应斌武. 人工智能整合临床检验数据预测慢性肾脏病进展[J/OL]. 中华临床实验室管理电子杂志, 2026, 14(03): 199-207.

阅读次数

全文

摘要

选择文件类型/文献管理软件名称

选择包含的内容

Research progress and application prospects of artificial intelligence in hematology analyzers re-examination workflow

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

Research progress and application prospects of artificial intelligence in hematology analyzers re-examination workflow