With the rapid advancement of artificial intelligence(AI)technology,its applications in the medical field have been expanding significantly.In the realm of hematopathological diagnosis,the advent of whole slide imaging(WSI)technology has paved the way for AI-based automatic slide reading,which is gradually transforming traditional diagnostic approaches.By establishing and training sophisticated algorithm models,AI,particularly deep learning and machine learning,can accurately and swiftly identify and analyze abnormal cells or tissues,as well as correctly inter-pret immunohistochemical staining results.This not only alleviates the workload of hematopathologists but also shortens the reporting cycle.As a result,it holds substantial application value in the fields of bone marrow pathology and bone marrow cell morphology diagnosis.Moreover,AI also demonstrates broad potential in the analysis of results from flow cytometry,chromosome karyotyping,and next-generation sequencing.Overall,the integration of AI technology is of great significance in propelling the development of intelligent diagnosis in hematopathology.This article aims to provide a comprehensive review of the application of AI in hematopathological diagnosis,and to explore its current status and future prospects.

With the rapid advancement of artificial intelligence(AI)technology,its applications in the medical field have been expanding significantly.In the realm of hematopathological diagnosis,the advent of whole slide imaging(WSI)technology has paved the way for AI-based automatic slide reading,which is gradually transforming traditional diagnostic approaches.By establishing and training sophisticated algorithm models,AI,particularly deep learning and machine learning,can accurately and swiftly identify and analyze abnormal cells or tissues,as well as correctly inter-pret immunohistochemical staining results.This not only alleviates the workload of hematopathologists but also shortens the reporting cycle.As a result,it holds substantial application value in the fields of bone marrow pathology and bone marrow cell morphology diagnosis.Moreover,AI also demonstrates broad potential in the analysis of results from flow cytometry,chromosome karyotyping,and next-generation sequencing.Overall,the integration of AI technology is of great significance in propelling the development of intelligent diagnosis in hematopathology.This article aims to provide a comprehensive review of the application of AI in hematopathological diagnosis,and to explore its current status and future prospects.

Bone marrow biopsy is one of the important means of hematopathological diagnosis, which has decisive diagnostic significance for various benign and malignant lymphohematopoietic system diseases. Its diagnostic value includes morphological observation, immunohistochemistry, genetics, and molecular biology testing. Owing to the unique nature of bone marrow biopsy, decalcification is an essential step in the pre-treatment process. Its purpose is to remove calcium from bone tissue, preserve intact collagen fiber components, facilitate tissue sectioning, and prevent tissue detachment during staining. If bone marrow biopsy lacks sufficient decalcification, preparing a section is difficult. Conversely, if decalcification is excessive, it can seriously disrupt tissue antigen activity. Therefore, a decalcification method with high decalcification efficiency and mild antigen damage is essential for bone marrow biopsy. This article introduces a bone marrow biopsy tissue decalcification method with high efficiency and less antigen loss: decalcification is performed at room temperature with 12% formic acid and 8% hydrochloric acid decalcification solution on a shaker.