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.