{"title":"[Application and Prospect of Artificial Intelligence and 3D Histopathology in the Diagnosis of Lymphoma--Review].","authors":"Tian Yu, Zi-Bo Yang, Meng-Yao Kang, Li-Ting Chen","doi":"10.19746/j.cnki.issn1009-2137.2026.04.043","DOIUrl":null,"url":null,"abstract":"<p><p>The rapid and accurate diagnosis of lymphoma is essential for understanding its pathogenesis, progression, and treatment outcomes. Compared to traditional diagnostic methods, artificial intelligence (AI) demonstrates superior data processing efficiency, delivering precise and consistent results. Meanwhile, three-dimensional (3D) histopathology provides a more comprehensive and detailed perspective of the disease. Consequently, the application of AI and 3D histopathology in lymphoma diagnosis is increasingly prevalent, addressing several challenges in current clinical practice. These technologies mitigate limitations related to diagnostic techniques and sample collection, reducing diagnostic turnaround time, and enhance accuracy. This review highlights recent advancements in AI and 3D histopathology for lymphoma diagnosis, while also discusses the remaining limitations and challenges in their implementation.</p>","PeriodicalId":35777,"journal":{"name":"中国实验血液学杂志","volume":"34 4","pages":"1222-1225"},"PeriodicalIF":0.0000,"publicationDate":"2026-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国实验血液学杂志","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.19746/j.cnki.issn1009-2137.2026.04.043","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
The rapid and accurate diagnosis of lymphoma is essential for understanding its pathogenesis, progression, and treatment outcomes. Compared to traditional diagnostic methods, artificial intelligence (AI) demonstrates superior data processing efficiency, delivering precise and consistent results. Meanwhile, three-dimensional (3D) histopathology provides a more comprehensive and detailed perspective of the disease. Consequently, the application of AI and 3D histopathology in lymphoma diagnosis is increasingly prevalent, addressing several challenges in current clinical practice. These technologies mitigate limitations related to diagnostic techniques and sample collection, reducing diagnostic turnaround time, and enhance accuracy. This review highlights recent advancements in AI and 3D histopathology for lymphoma diagnosis, while also discusses the remaining limitations and challenges in their implementation.