APL is a medical emergency associated with an early mortality rate of up to 30%, necessitating rapid and accurate diagnosis (45). Morphologically, APL is characterized by abnormal promyelocytes with heavy granulation and Auer rods (46). While cytomorphology remains the fastest diagnostic modality, definitive confirmation often requires cytogenetic and molecular testing (47–49).

AI models, particularly CNNs, have been developed to assist in rapid morphological screening. For instance, Qiao et al (50) proposed a compact CNN that distinguished promyelocytes from normal nucleated cells with accuracies of 96.53 and 99.20% on two public datasets (APL-Cytomorphology-LMU and -JHH; The Cancer Imaging Archive). Eckardt et al (14) developed a multi-stage DL platform for automated bone marrow smear analysis, reporting an average precision and recall of 0.97 for cell segmentation and an AUROC of 0.8575 and 0.9585 for distinguishing APL from non-APL AML and healthy donors, respectively, enabling timely diagnosis of APL and early intervention for patients. Manescu et al (51) introduced a multi-instance learning (MILLIE) method, which detected APL with an area under curve (AUC) of 0.94±0.04 in peripheral blood smears and 0.99±0.01 in bone marrow smears without requiring cell-level annotations. Integrating MILLIE into clinical workflows may markedly improve detection throughput and reduce cognitive human errors. Recently, our previous study exhibited a CELLSEE model for APL screening, which demonstrated a joint diagnostic performance achieving 93.00% accuracy and 100% recall at magnifications of ×10 and ×100 in batch samples, thereby ensuring no APL cases were missed (52). The aforementioned models for rapid diagnosis of APL demonstrated satisfactory morphological recognition performance (Table I).

AML represents the most common type of AL, with an incidence rate of ~33.5% among all leukemias (2). The morphological characteristics of AML blasts are as follows: i) The cell size is moderate; ii) the nucleus is generally round or ovoid, and may exhibit indentation and folding; iii) the nuclear chromatin is finely granular and lacy with distinct nucleoli; iv) the amount of cytoplasm varies, is basophilic, and frequently contains variable numbers of azurophilic granules; and v) a small proportion of blasts may show Auer rods (37).

Diagnosis is primarily based on blast cell counts in bone marrow or peripheral blood (53), with subclassification into M0-M7 according to French-American-British classification criteria (54). However, cellular morphological examination is an efficient but highly demanding task, relying on well-trained and experienced medical professionals. Therefore, there is a need for an automated AML classification system that overcomes these issues.

Initial applications of ML in this domain relied on traditional algorithms. For instance, techniques such as K-means and fuzzy C-means clustering were primarily employed for the segmentation of nuclei and cells in bone marrow smears, serving as foundational tools for the screening and identification of AML (55,56). The advent of DL has markedly improved diagnostic performance. Huang et al (57) combined preprocessing algorithms with a CNN to achieve 99% accuracy in AML classification, demonstrating the viability of transfer learning in this context. Building on these preprocessing techniques, several integrated DL frameworks have been developed for comprehensive AML recognition and classification. Wang et al (58) constructed an ImageNet-pretrained CNN for recognizing aplastic anemia (AA), myelodysplastic syndromes (MDS) and AML, achieving an AUC of 0.968, accuracy of 0.929 and sensitivity of 0.857 for AML recognition in the testing set, providing a convenient tool for clinical doctors to distinguish AA, MDS and AML. More advanced DL pipelines have since been developed to address increasingly complex diagnostic tasks. Eckardt et al (59) extended DL applications to predict nucleophosmin 1 mutation status directly from morphological images, attaining an AUROC of 0.92. Yu et al (60) developed AMLnet, a DL pipeline capable of AML subtype discrimination with image- and patient-level AUCs of 0.885 and 0.921, respectively, offering a rapid prescreening and decision support tool for morphological pathologists. Other notable models include a hybrid deep convolutional autoencoder-CNN model by Elhassan et al (61), which reported 97% accuracy, 97% sensitivity and 98% precision for classifying atypical AML cells. In the realm of image segmentation, Zhang et al (62) employed an AML conditional generative adversarial network (AMLcGAN) for blast segmentation, achieving a precision of 0.8496 and a recall of 0.8831. The application of DL has also extended to peripheral blood smear analysis. For instance, an auxiliary classification GAN evaluated by Zhang et al (63) achieved 97.1% accuracy for AML screening. More recently, Aby et al (64) conducted a comparative study of activation functions within deep neural networks, specifically InceptionV3, ResNet50v2 and VGG16, for AML subtype classification. The findings indicated that the Gaussian Error Linear Unit function yielded the highest accuracy of 94.02% when implemented in InceptionV3. Cheng et al (65) developed an AI model using 65,039 myeloblast images from 205 patients with AML to identify RUNX1::RUNX1T1 fusion gene abnormalities via cell morphology, which achieved a maximum sensitivity of 95.65% and specificity of 92.68% under different thresholds, enabling effective recognition of this genetic alteration. Collectively, these studies underscore the strong capability of DL-based systems in enhancing the accuracy and efficiency of AML diagnosis and classification (Table II).

ALL accounts for roughly 9.2% of all leukemia cases and primarily affects children, featuring excessive proliferation of lymphocytes in the bone marrow. Despite a favorable cure rate in the pediatric population, ALL still accounts for 6.7% of cancer-related deaths among all age groups (2,66). Diagnosis relies on cytomorphology, immunology and genetics, but resource limitations often restrict access to advanced assays, making morphological assessment indispensable. The morphological features of ALL blasts are as follows: i) The cell size is moderately small to small; ii) the nuclei are mostly round with coarse granular or blocky nuclear chromatin, which is coarser than that of AML blasts; iii) nucleoli are generally small and distinct, though inconspicuous in some cases; iv) the cytoplasm is extremely scanty and basophilic, occasionally containing a few azurophilic granules; and v) basket cells and mitotic figures are commonly observed (37). With the continuous development of AI, there have been numerous studies on morphological computer-aided diagnosis for ALL (Table III).

Early applications of DL demonstrated excellent performance in the diagnosis of ALL. Shafique et al used a DL CNN for ALL diagnosis, achieving 100% sensitivity, 98.11% specificity and 99.50% accuracy, enabling high performance without the need for microscopic image segmentation (67). Rehman et al (68) also employed a CNN-based model for ALL-training, with a reported accuracy of 97.78% for the diagnosis of ALL and subtype classification. These foundational studies catalyzed rapid growth in AI-based ALL diagnosis using cell image recognition.

Subsequent studies in 2021 explored diverse architectural approaches. Rezayi et al (69) compared ResNet-50, VGG-16 and a proposed convolutional network, achieving accuracies ranging from 81.63 to 84.62%. The proposed network is simpler than the two pretrained networks and can be employed by pathologists to recognize ALL. Furthermore, an optimized IoT-friendly neural network achieved 97.5% accuracy for ALL classification (70), while a ViT-CNN ensemble model reached 99.03% accuracy in distinguishing blasts from normal cell images to support diagnosis (71). Additionally, a Random Forest classifier integrated with image enhancement yielded 98% accuracy for recognition of ALL (72).

In 2022, several innovations were introduced. The ALL Detector achieved 98% accuracy in distinguishing patients with ALL from healthy individuals, serving as a screening tool (73), and a deep neural network-based model that employs depth-wise convolution with different dilation rates to classify nucleated cell images and yielded a classification accuracy of 91.13%, which is useful for ALL categorization (74). Among traditional ML algorithms, such as K-nearest neighbor, support vector machine (SVM), random forest and naive bayes, SVM performed best with 90.0% accuracy for predicting ALL (75). A Bayesian-optimized CNN achieved 100% across all evaluation metrics, showing superiority over existing DL systems developed for ALL diagnosis (76). A ResNet101-9 ensemble model attained 85.11% accuracy, which performed well in classifying ALL (77), and a hybrid artificial neural network-feedforward neural network-SVM model achieved 100% accuracy for diagnosis of ALL (78).

In 2023, model development continued with varied architectures. A VGG16 model combined with SVM and multilayer perceptron (MLP) classifiers yielded a training accuracy of 92.27% and a validation accuracy of 85.62%, achieving fast and accurate diagnosis (79). The hybrid CNN-RF and CNN-XGBoost systems exhibited 100% AUC, accuracy and sensitivity, thereby supporting effective early diagnosis of ALL (80). Furthermore, a Deep Skip Connections-Based Dense Network tailored for ALL diagnosis obtained 99.37% accuracy, 99.71% sensitivity, 99.03% specificity and an AUC of 99.37%, demonstrating the potential of an effective tool for early and accurate diagnosis of ALL (81).

In 2024, the focus shifted toward model refinement and clinical applicability. An ensemble-ALL model achieved notable performance, with accuracy, precision, recall, F1-score and kappa scores of 96.26, 96.26, 96.26, 96.25 and 91.36%, respectively; this model has made valuable contributions to medical image recognition, particularly for the diagnosis of ALL (82). A Deep Dilated Residual CNN demonstrated minimal computational complexity and improved the discrimination of crucial features for accurate multi-class blood cell image recognition, with an accuracy rate of 91.98% (83). Another deep neural network-based model has a classification accuracy of ~97% for ALL and lymphocyte subtypes (84). A method leveraging Hilbert Huang supervision achieved 98.7% accuracy, 99.3% sensitivity, and 98.1% specificity for classifying normal and blast ALL cell images, which could assist in the screening of ALL (85). Additionally, a hybrid detection model combining SVM with particle swarm optimization reported an accuracy of 93%, which is superior to stand-alone algorithms and exhibiting increased overall performance, an improved confusion matrix and a higher detection rate of ALL (86).

In 2025, Mei et al (87) carried out two related studies on lymphoblastic leukemia detection: in one study, they proposed a lightweight model focusing on throughput (111 slides per second) with a maintained accuracy of 92.51%, reflecting the trend of developing clinically applicable efficient models. Furthermore, Mei et al (88) built a high-quality dataset containing 1,794 microscopic images and developed the spatially-guided learning framework, which solved key challenges in whole-slide leukemia detection and achieved a mean average precision of 95.9% for ALL. In addition, Anand et al (89) proposed a deep optimized CNN model for early ALL diagnosis, which, optimized with the Adam optimizer among other methods, attained an accuracy of 0.96 and a precision of 0.95 after fine-tuning the corresponding hyperparameters.

Despite these advances, to the best of our knowledge, no model has undergone rigorous external validation in real-world cohorts, and challenges related to model interpretability, data heterogeneity and computational efficiency still persist.

A comparison of AI methodologies reveals distinct trade-offs among data requirements, interpretability, computational efficiency and clinical roles (Table IV). Traditional ML models offer high interpretability and low computational cost but require manual feature engineering and plateau in performance. Classic deep CNNs achieve state-of-the-art accuracy through automated feature learning but suffer from low inherent interpretability and high computational demands. Lightweight architectures balance efficiency and accuracy, making them suitable for point-of-care applications. Hybrid models combine the strengths of DL and ML, offering moderate interpretability and robust performance on smaller datasets. Ensemble methods, while often achieving top benchmark performance, entail high complexity and risk of overfitting. Generative models are valuable for data augmentation and unsupervised segmentation but may produce artifacts. Clinically, lightweight CNNs are preferable for rapid screening in resource-limited settings, whereas hybrid or ensemble models may serve as high-precision diagnostic aids in well-equipped laboratories.