Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.

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Learn more: PMC Disclaimer | PMC Copyright Notice Ann Hematol . 2026 Apr 14;105(5):241. doi: 10.1007/s00277-026-06996-0 Search in PMC Search in PubMed View in NLM Catalog Add to search Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management Zhujin Li Zhujin Li 1 Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032 China Find articles by Zhujin Li 1, # , Jie Zhao Jie Zhao 1 Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032 China Find articles by Jie Zhao 1, # , Lifang Huang Lifang Huang 1 Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032 China 2 Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China Find articles by Lifang Huang 1, 2 , Xiuhua Chen Xiuhua Chen 3 The Key Laboratory of Molecular Diagnosis and Treatment of Hematological Diseases of Shanxi Province, 382 Wuyi Road, Taiyuan, Shanxi Province China 4 The Second Hospital of Shanxi Medical University, Taiyuan, China Find articles by Xiuhua Chen 3, 4 , Jia Wei Jia Wei 2 Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China Find articles by Jia Wei 2 , Weiwei Tian Weiwei Tian 1 Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032 China 3 The Key Laboratory of Molecular Diagnosis and Treatment of Hematological Diseases of Shanxi Province, 382 Wuyi Road, Taiyuan, Shanxi Province China Find articles by Weiwei Tian 1, 3, ✉ Author information Article notes Copyright and License information 1 Shanxi Bethune Hospital, Shanxi Academy of Medical Sciences, Third Hospital of Shanxi Medical University, Tongji Shanxi Hospital, Taiyuan, 030032 China 2 Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030 China 3 The Key Laboratory of Molecular Diagnosis and Treatment of Hematological Diseases of Shanxi Province, 382 Wuyi Road, Taiyuan, Shanxi Province China 4 The Second Hospital of Shanxi Medical University, Taiyuan, China ✉ Corresponding author. # Contributed equally. Received 2025 Dec 24; Accepted 2026 Apr 1; Issue date 2026. © The Author(s) 2026 Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/ . PMC Copyright notice PMCID: PMC13076378  PMID: 41974984 Abstract Artificial intelligence (AI) is increasingly being explored as a tool to support more precise and dynamic management in the diagnosis and treatment of hematologic malignancies. Unlike previous reviews focused on single disease types or isolated technological pathways, this paper provides a comprehensive overview of AI’s current applications and latest advancements in diagnosis, classification, prognosis assessment, and treatment decision-making for leukemia, lymphoma, multiple myeloma, and myelodysplastic syndromes. It encompasses key technical pathways, including morphology, imaging, flow cytometry, and multimodal data fusion, and further constructs an AI-driven dynamic diagnosis and treatment system along with its integrated deployment framework for electronic health records. This framework is intended to illustrate how multimodal data integration, dynamic risk assessment, and more coordinated longitudinal management could be supported within an integrated workflow. This integrated dynamic model provides a structured roadmap for intelligent, end-to-end management of hematologic malignancies and holds promise for advancing future intelligent clinical pathways. While AI demonstrates significant potential to enhance diagnostic consistency, optimize risk stratification, and enable personalized treatment, its development remains constrained by challenges such as data bottlenecks, insufficient cross-institutional model generalization, and ethical oversight. Future efforts should focus on advancing multicenter prospective validation, adhering to international standards like TRIPOD + AI, and refining data privacy, model interpretability, and ethical oversight systems. These advances may help support the future development of more personalized and dynamic patient management strategies within an evidence-based framework. Keywords: Artificial intelligence; Decision support systems, clinical; Electronic health records; Hematologic neoplasms; Leukemia; Lymphoma Introduction Hematologic malignancies are highly heterogeneous, with their development driven by genetic and epigenetic alterations, tumor microenvironment remodeling, and clonal evolution, resulting in significant prognostic variations and high recurrence risks [ 1 , 2 ]. Although targeted, immunological and cellular therapies are advancing [ 2 ], there is still a considerable gap in the existing system for key aspects such as diagnosis, prognosis evaluation and treatment follow-up, and for precise, whole-process management [ 2 , 3 ]. Diagnosis and classification require comprehensive evaluation of morphological and immunophenotypic findings, yet current methods remain heavily reliant on manual interpretation, which is inefficient and prone to subjective bias and inter-center variability [ 3 , 4 ]. Prognosis assessment predominantly depends on static baseline metrics and staging systems, failing to capture the dynamic evolution of tumor microenvironment and clonal architecture [ 1 , 2 ]. In therapeutic decision-making and efficacy monitoring, biomarkers such as minimal residual disease (MRD) lack cross-platform standardized and transferable criteria, while research on drug resistance mechanisms and novel targets has yet to establish actionable clinical pathways [ 5 , 6 ]. Artificial intelligence (AI) is being increasingly applied to the diagnosis and treatment of hematologic malignancies through four key applications: First, automated identification of cellular and histological patterns for classifying peripheral blood smears, bone marrow morphology, and pathological Sects. [ 7 – 9 ]; Second, integration of multi-omics and multimodal data using machine learning or deep learning to build diagnostic and prognostic models incorporating genomic, transcriptomic, and radiomics data [ 10 ]; Third, dynamic prediction based on longitudinal data, employing time-series or joint models to assess survival outcomes, recurrence risks, and treatment efficacy [ 11 , 12 ], Fourth, embedding decision support systems into electronic health records (EHRs) to provide real-time risk alerts and treatment recommendations within clinical workflows [ 13 , 14 ]. AI has shown potential in extracting patterns from high-dimensional, multimodal data that are difficult for human eyes to discern, particularly in enhancing diagnostic consistency, refining prognostic stratification, and supporting personalized treatment adjustments. However, its development remains constrained by challenges such as biased training datasets, insufficient cross-center generalization, weak “black box” explainability, and inadequate ethical oversight [ 15 – 17 ]. Relying solely on AI is insufficient to reshape clinical pathways; it must be advanced in tandem with multi-center data governance, interpretable algorithms, human-machine collaboration, and prospective clinical trials [ 15 , 17 – 19 ]. Unlike previous reviews that focused on single diseases or technical approaches, this study systematically examines AI advancements in diagnosing leukemia, lymphoma, multiple myeloma, and myelodysplastic syndromes, analyzing their diagnostic classification, prognosis assessment, and treatment decision-making capabilities while evaluating their technical strengths and limitations. Building on this foundation, we propose an AI-driven dynamic diagnostic-therapeutic system and its integration framework with electronic health records, providing insights for developing and optimizing intelligent clinical pathways for hematologic malignancies. Development of artificial intelligence in the field of hematologic malignancies The application of artificial intelligence in hematologic malignancies has evolved through distinct phases: the traditional machine learning and early omics era, the high-dimensional machine learning and deep learning exploration phase, the multimodal learning and longitudinal modeling stage, and finally the large language model and clinical decision support phase, as illustrated in Fig. 1 . Fig. 1. Open in a new tab The development of artificial intelligence in the field of hematologic malignancies. This diagram illustrates the groundbreaking progress of AI in the field of hematologic malignancies and its corresponding clinical impact. The timeline spans from 1999 to the present, with each achievement laying the foundation for an intelligent and verifiable era of precision diagnostics and therapeutics. This figure was created with Adobe Illustrator Traditional machine learning and early omics phase (1999–2014) This phase primarily utilizes high-throughput yet single-modal early omics data, such as gene expression chips, with traditional machine learning (ML) algorithms like support vector machines (SVM) and random forests (RF) being the main applications [ 20 – 23 ]. The landmark achievement came in 1999 when Golub et al. pioneered the use of DNA microarrays and machine learning to automatically distinguish acute myeloid leukemia (AML) from acute lymphoblastic leukemia (ALL) [ 20 ], demonstrating for the first time that leukemia subtypes could be identified solely through gene expression profiles. Bullinger et al. utilized gene expression profiles to establish prognostic subtypes in human AML, achieving further stratification of risk populations that were previously unidentifiable through conventional cytogenetics [ 22 ]. This phase has initially achieved automatic disease classification and risk stratification but relies on manual feature screening, making it difficult to fully exploit high-dimensional, nonlinear omics data. High-dimensional machine learning and deep learning exploration phase (2015–2019) With advances in computing power and data scale, high-dimensional machine learning and deep learning (DL) have been progressively applied to hematologic oncology research. At this stage, most deep learning applications remain focused on specific tasks and small-sample cohorts, with the field still in its methodological exploration phase. Omics: High-dimensional molecular signature-driven prognostic stratification In omics analysis, researchers integrate high-dimensional molecular features, such as mutation profiles and transcriptomes, into prognostic models to refine risk stratification for hematologic malignancies like AML [ 24 – 26 ]. Subsequent studies in solid tumor cohorts demonstrated deep learning’s superiority over traditional linear models in high-dimensional data integration and survival subgroup classification, providing a replicable technical paradigm for developing multi-omics deep learning models in hematologic oncology [ 27 ]. Image: Deep learning-driven analysis of blood pathology images In image analysis, convolutional neural networks (CNNs) have been applied to blood pathology imaging for automated cell morphology recognition and classification [ 7 , 28 – 30 ]. A landmark study by Matek et al. demonstrated that their CNN model, developed from AML peripheral blood smears, achieved near-human expert performance in leukemia cell identification [ 7 ]. These advancements have significantly enhanced the objectivity, consistency, and efficiency of cell morphology diagnosis. Multimodal learning and longitudinal modeling phase (2020–present) With the accumulation of multi-omics and clinical data, research has shifted focus from single-indicator or single-modal analysis to integrating clinical features and multi-omics information to construct comprehensive prognostic models, while exploring dynamic risk profiles of disease progression [ 31 , 32 ]. For instance, studies have developed predictive models outperforming traditional methods by combining clinical parameters and RNA sequencing data from multiple myeloma (MM) patients [ 32 ]. Meanwhile, AI is beginning to analyze time-series data in EHRs to predict disease recurrence or treatment response early, enabling more precise individualized treatment decisions [ 33 ]. The phase of large language models and clinical decision support (2024–present) Large language models (LLMs), exemplified by GPT-4, are being explored as potentially useful tools for clinical decision support. They are well suited to processing unstructured text and synthesizing information from medical literature and guidelines for complex cases, demonstrating significant utility in differential diagnosis and the systematic comparison of medical oncology guidelines [ 34 , 35 ]. Recent studies have also explored combining LLMs with specialized medical tools to support more structured task execution; however, these systems remain at an early stage, and their reliability, calibration, safety, and real-world clinical utility in hematologic practice remain to be established [ 36 , 37 ]. Application of artificial intelligence in the diagnosis and treatment of hematologic malignancies Leukemia Leukemia is a highly heterogeneous malignant tumor with multiple subtypes originating from hematopoietic stem cells [ 38 ]. Diagnosis primarily follows the MICM integrated pathway (morphology, immunology, cytogenetics, and molecular biology), which heavily relies on manual interpretation and lacks standardization [ 39 , 40 ]. Furthermore, significant genetic and phenotypic variation in leukemia cells poses substantial challenges for prognostic assessment and personalized treatment decisions. The development of AI holds the promise of addressing these bottlenecks. Diagnosis and disease classification The application of AI in leukemia diagnosis and classification is evolving from traditional ML that relies on manually designed features to DL with end-to-end feature learning, and further advancing toward multimodal data fusion. ML models that depend on expert-defined features offer strong interpretability and practical value in scenarios with limited data resources. A representative work is the ALLSorts hierarchical classifier proposed by Schmidt et al., which uses RNA sequencing data to identify molecular subtypes of B-cell acute lymphoblastic leukemia (B-ALL), achieving 84%−92% accuracy across a multicenter cohort encompassing 18 subtypes [ 41 ]. The innovation of this model lies in its meta-subtype hierarchical architecture, which can assign cases with ambiguous classifications to higher-level categories, thereby enhancing clinical applicability. However, its performance is limited by the quality of RNA-seq data, and its ability to identify rare subtypes remains insufficient due to sample scarcity. In contrast, DL models employ convolutional neural networks to achieve end-to-end feature learning, enabling automatic extraction of complex disease-related patterns from raw data, demonstrating significant potential [ 42 ]. For instance, Yadav et al.’s 3SNet model achieved 84% to 93.8% accuracy in leukemia cell morphology classification by integrating grayscale, gradient direction histogram, and local binary pattern features from cellular microscopy images [ 43 ]. However, the clinical utility of morphological AI is often constrained by pre-analytical variability across laboratories, because subtle differences in fixation, staining intensity, and scanning settings can introduce domain shift that disproportionately degrades reliability for rare or dysplastic cells [ 44 ]. Following the single-modal approaches in ML and DL, current research is shifting toward multi-source data fusion. A notable example is the work by Hybel et al., who integrated imaging flow cytometry (IFC) with DL by employing CNNs to jointly model bright-field images and DNA morphology of leukemia stem cells and normal hematopoietic stem cells in AML patients, achieving a discriminant accuracy of up to 93.42% [ 45 ]. This demonstrates the value of multi-channel imaging information fusion in precise cell-level classification. However, the study’s limited sample size and inherent cellular heterogeneity remain critical challenges for its robust clinical application. Beyond imaging flow cytometry, machine learning has also been applied to conventional multicolor flow cytometric datasets to alleviate the time-consuming and operator-dependent nature of manual MRD interpretation. In B-ALL, an AI-enhanced pipeline reduced manual MRD review time to approximately 1 min per case while maintaining excellent agreement with conventional expert analysis, highlighting a practical route to improve the accessibility of flow-based MRD testing [ 46 ]. However, the clinical readiness of flow cytometry is limited less by model design than by cross-platform non-equivalence, as variation in antibody panels, compensation, instrument settings, and gating conventions can cause data drift that requires continuous quality control and expert adjudication [ 47 ]. Prognosis assessment and treatment decision Most current leukemia prognosis models still rely on static pre-treatment metrics. For instance, Eckardt et al. conducted a multicenter study involving 1,383 AML patients, systematically comparing nine supervised learning algorithms that integrated multidimensional data to predict complete response rates and 2-year overall survival (OS) [ 48 ]. While the study quantified the importance of multi-gene features, its retrospective data nature limited direct clinical translation. Subsequent research by the team [ 49 ] further used interpretable AI frameworks, such as Shapley Additive Explanations (SHAP), for age-stratified analysis, revealing that the prognostic weights of key genes, such as NPM1 and TP53, dynamically change with age. This highlights the need to consider age covariates in risk assessment. Beyond baseline prognostic modeling, prospective evidence suggests that functional drug profiling may support treatment selection in AML. The SMARTrial showed that ex vivo drug response reports could be returned within 7 days for most participants, and in an AML validation cohort, incorporation of drug response profiles improved ELN-2022 risk stratification for adverse-risk patients [ 50 ]. However, translating ex vivo sensitivity signals into in vivo efficacy remains challenged by a microenvironment gap, as stromal protection and immune interactions within the bone marrow niche can reshape drug vulnerabilities and thereby decouple predicted sensitivity from real-world response [ 51 ]. The transformation in prognostic assessment stems from incorporating dynamic biomarkers such as MRD into predictive models. Jeha et al.’s study exemplifies this approach, integrating multi-timepoint MRD monitoring with detailed molecular subtypes in a cohort of 598 pediatric ALL patients, establishing a truly dynamic biomarker-based framework for prognostic evaluation and treatment decision-making. The study measured MRD levels in bone marrow and peripheral blood multiple times during early induction and consolidation phases, using Kaplan-Meier survival analysis and multivariate Cox regression to assess event-free survival and overall survival outcomes [ 52 ]. This model enables risk stratification based on real-time MRD updates during treatment, facilitating risk-adaptive therapeutic adjustments. AI applications also demonstrate significant potential in emerging therapies. The deep learning model RCMNet, developed by Zhang et al., achieved 99.63% accuracy in CAR-T cell morphology recognition by integrating attention mechanisms, providing an automated tool for quality control in treatment processes [ 53 ]. AI applications in leukemia are currently shifting the focus of diagnosis and treatment from static baseline assessments to dynamic risk evaluation and personalized decision-making, driven by longitudinal indicators such as MRD. While technological advancements continue to be implemented in both morphological and flow cytometry domains, cross-center consistency and standardization remain critical gaps. To realize clinical value, more rigorous external validation and workflow-level integration are essential. We have compiled representative studies from recent years, as shown in Table 1 . Table 1. Representative recent advances in artificial intelligence for leukemia Disease subtypes Objective Data type Model/Methodology Performance Strength Limitations Reference B-ALL Molecular subtype classification of B-ALL RNA-seq ALLSorts Accuracy: 84.0%–92.0% Training across multiple cohorts; interpretable hierarchical label structure Performance depends on RNA-seq data quality; retrospective design [ 41 ] Acute leukemia CAR-T cell morphology analysis Single-cell peripheral blood microscopy images RCMNet Accuracy > 99.0% Hybrid CNN and transformer model improves morphology recognition. Small single-center CAR-T image set limits generalization [ 53 ] ALL CNN prediction of leukemia using Chi-square–selected microarray features Microarray gene data CNN Accuracy > 99.0% Use of k-fold cross-validation (internal robustness) Limited model generalization [ 54 ] Leukemia Improving the diagnostic accuracy for leukemia Multi-omics and clinical features RF, Decision Tree, NB, LR, GB, RNN and FNN RNN accuracy: 98.0%; GB: 97.0% Multiomics integration; broad comparison across ML and DL Lacks external validation [ 55 ] BCP-ALL Subgrouping and diagnostics support RNA-seq and clinical diagnostics clinALL pipeline Best models achieve F1 > 97.0% Integration of genomic and clinical data Retrospective design; dependent on RNA-seq availability [ 56 ] ALL DL-based blood cancer prediction Peripheral blood smear microscopy images ResNetRS50, RegNetX016, AlexNet, Convnext, EfficientNet, Inception_V3, Xception, and VGG19 ResNetRS50 accuracy: 97.0% (internal validation) Open dataset; broad model comparison Single public dataset; lacks external validation [ 57 ] Open in a new tab Abbreviations: B-ALL B-cell acute lymphoblastic leukemia, RNA-seq RNA sequencing, CNN convolutional neural network, RF random forest, NB naive Bayes, LR logistic regression, GB gradient boosting, RNN recurrent neural network, FNN feedforward neural network, ML machine learning, DL deep learning, BCP-ALL B-cell precursor acute lymphoblastic leukemia Lymphoma Lymphoma is a highly heterogeneous malignant tumor originating from lymphocytes [ 58 ]. This disease encompasses over 100 subtypes, and its accurate diagnosis requires integrating clinical, morphological, immunophenotypic, and molecular genetic information, with pathological interpretation being crucial [ 59 ]. Given the significant variations among these subtypes, stratification of prognosis and selection of treatment strategies becomes more complex and challenging. AI can identify microscopic patterns that are difficult for human eyes to discern, thereby improving process efficiency and aiding in decision-making for complex cases [ 60 ]. Diagnosis and disease classification In the field of pathological image analysis, DL has achieved automated feature extraction and classification of H&E-stained slides while identifying morphological patterns associated with specific molecular alterations. For instance, the image-based DL algorithm developed by Perry et al. can directly identify double/triple-hit lymphoma (DHL/THL) from H&E slides with a sensitivity of 100% and an area under the curve (AUC) of 0.95, outperforming traditional fluorescence in situ hybridization (FISH) detection [ 61 ]. The Carreras team used a ResNet-based CNN model to automatically distinguish follicular lymphoma (FL) from reactive hyperplastic tissue in conventional H&E whole-slide images, achieving a model accuracy of 99.80% through rigorous data segmentation and case-level validation [ 62 ]. These achievements highlight the efficiency potential of automated histomorphological analysis, though most studies remain based on single-center, small-sample data. External validation in large, prospective, multicenter cohorts is urgently needed to confirm their generalizability and clinical applicability. In molecular subtype identification, the standardized interpretation of genomic data remains a core challenge for precise lymphoma genotyping [ 63 ]. Unlike solid tumors, molecular data from lymphomas are primarily used for classification rather than targeted therapy, leading to inconsistent interpretation frameworks and significant interobserver variability. AI technologies show promise in providing standardized solutions. For instance, Shen et al. developed a simplified 38-gene classifier named “LymphPlex” by integrating genome sequencing, RNA sequencing, and FISH techniques. This classifier successfully classified diffuse large B-cell lymphoma (DLBCL) into seven molecular subtypes with distinct prognostic characteristics, validated across over 1,000 patient cases [ 64 ]. Bobee et al. established a classification system for seven B-cell non-Hodgkin lymphoma (B-NHL) subtypes using expression profiles of approximately 130 genes and RF [ 65 ]. Genomic classifiers can be fragile to batch effects and cohort shift, because differences in library preparation, sequencing depth, and variant calling pipelines may change feature distributions, and performance may deteriorate when deployed in underrepresented patient groups without rigorous external calibration [ 66 ]. Prognosis assessment and treatment decision In the development of prognostic models, multimodal data fusion is emerging as a key trend. Lee et al. developed a deep learning model integrating digital pathology and clinical data to predict immune chemotherapy responses in diffuse large B-cell lymphoma (DLBCL) patients, achieving a validation AUC of approximately 0.86 [ 67 ]. The model’s innovation lies in enhancing robustness and interpretability through knowledge distillation and attention mechanisms. However, given its limited sample size and retrospective design, comprehensive validation incorporating genomic data is required. In radiomics, Jiang’s team developed a multicenter, interpretable deep learning system named DeepENKTCL. By automatically segmenting PET/CT images of extranodal NK/T-cell lymphoma (ENKTCL), the system extracts radiomics and topological features, then provides explanations using the SHAP framework. The resulting FusionScore outperforms traditional models in stratification for progression-free survival (PFS) and OS [ 68 ]. Beyond prognostic radiomics, PET/CT-based AI has also been developed to automate lymphoma staging under established clinical systems. Li et al. developed a multi-view deep learning model based on baseline 18 F-FDG PET/CT maximum-intensity-projection (MIP) images to classify limited versus advanced stage, achieving accuracy comparable to early-career nuclear medicine physicians while substantially reducing interpretation time [ 69 ]. Hasanabadi et al. further showed that radiomics-based machine learning could reproduce Lugano staging in a multicenter externally validated cohort, with extranodal features improving discrimination and survival analysis supporting its clinical relevance [ 70 ]. In contrast, traditional machine learning based on large-scale real-world cohorts has demonstrated robust performance. Zha et al. developed the FLIPI-C model using XGBoost to predict 24-month progression of disease (POD24) from data of 1,938 FL patients across 17 centers. The model achieved AUC values of 0.76 in internal validation and 0.70 in external validation, outperforming existing benchmarks and providing a potential tool for early intervention in high-risk patients [ 71 ]. However, the model does not incorporate genomic information and relies on retrospective data, and further prospective studies are needed to confirm its clinical utility. In the field of CAR-T therapy prediction, Raj et al. proposed the unsupervised InflaMix model for predicting inflammatory responses. The INFLAmmation MIXture model successfully predicted 6-month PFS in three independent cohorts by integrating 14 pre-infusion laboratory and cytokine markers [ 72 ]. Its advantages include a single blood draw and simplification to 6 routine indicators, demonstrating strong clinical implementation potential. Current AI research in lymphoma primarily focuses on common subtypes such as diffuse DLBCL and FL, where PET/CT radiomics continues to demonstrate value in predicting treatment efficacy and stratifying risk. The core challenge lies in the limited generalization capacity of models due to insufficient samples of long-tail rare subtypes, which will require solutions through multi-center data collaboration and other technical approaches. We have compiled representative studies from recent years, as shown in Table 2 . Table 2. Representative recent advances in artificial intelligence for lymphoma Disease subtypes Objective Data type Model/Methodology Performance Strength Limitations Reference Malignant lymphoma Lymphoma types prediction 18 F-FDG PET images nnU-Net, GLRLM, GLCM and HU moments Accuracy > 94.0%; PPV: 91.0%; NPV > 95.0% Enhancement of superresolution; hybrid features; ensemble learning Lacks external validation [ 59 ] ENKTCL Prognostic risk stratification 18 F-FDG PET images Tumor segmentation model, PET/CT fusion model and prognostic prediction models Validation AUC: PFS 0.90, OS 0.92; Test AUC: PFS 0.82, OS: 0.87 Multicenter design; interpretable pipeline Retrospective design [ 68 ] FL POD24 risk prediction Baseline clinical and laboratory data XGBoost, FLIPI-C score POD24 AUC: 0.76 (internal)/0.70 (external) Multicenter design; independent external validation Retrospective design [ 71 ] NHL CAR-T outcomes prediction Preinfusion blood labs and cytokines InflaMix 6-month PFS AUC: 0.72–0.74 Simple blood draw biomarkers; multicenter external validation Retrospective design [ 72 ] PCNSL DL-assisted intraoperative discrimination of PCNSL H&E frozen whole slide images LGNet CNN External AUROC: 0.965/0.972 (PCNSL vs. glioma); 0.98/0.99 (PCNSL vs. non-PCNSL) Morphological interpretability; multicenter external validation Proof of concept only [ 73 ] DLBCL MYC translocation detection H&E whole slide images U-Net and RF Sensitivity: 93.0%; specificity: 52.0% Fast prescreening utility; multicenter external validation Modest specificity; signal from morphology only [ 74 ] Open in a new tab Abbreviations : 18 F-FDG 18 F-fluorodeoxyglucose, U-Net convolutional neural network architecture for biomedical image segmentation, nnU-Net no new net, GLRLM gray-level run-length matrix, GLCM gray-level co-occurrence matrix, HU moments, Hu’s invariant moments, PPV positive predictive value, NPV negative predictive value, ENKTCL extranodal NK/T-cell lymphoma, AUC area under the curve, PFS progression-free survival, OS overall survival, FL follicular lymphoma, POD24 disease progression within 24 months, NHL non-Hodgkin lymphoma, InflaMix inflammation mixture, PCNSL primary central nervous system lymphoma, H&E hematoxylin and eosin, CNN convolutional neural network, AUROC area under the receiver operating characteristic curve, DLBCL diffuse large B-cell lymphoma, MYC MYC proto-oncogene, bHLH transcription factor, RF random forest Multiple myeloma MM, a malignant tumor arising from clonal proliferation of plasma cells in the bone marrow, predominantly affects elderly patients. Its classic CRAB symptoms (hypercalcemia, renal impairment, anemia, and osteoporosis) severely compromise patients’ quality of life [ 75 ]. The challenges in early diagnosis, risk stratification, and treatment decision-making stem from the often insidious nature of bone lesions, uneven distribution of myeloma cells, and their molecular and clinical heterogeneity. AI offers new possibilities to overcome these limitations. Diagnosis and disease classification As the disease progresses, plasma cells in the bone marrow can enter the peripheral blood, forming circulating plasma cells (CPCs). Elevated CPC levels not only indicate an increased risk of extramedullary infiltration but also serve as a key indicator for prognosis assessment and MRD monitoring [ 76 ]. Chen et al. developed the Morphogo CNN morphological analysis system, trained on over 300,000 cell images, achieving 99.6% accuracy in CPC detection [ 76 ]. Notably, the model threshold can be adjusted according to clinical needs. At a lower threshold of 0.60, sensitivity for CPC detection increased to 96.2%, but specificity decreased to 78.0%, raising the risk of false positives. This threshold was empirically chosen to illustrate high-sensitivity screening rather than determined through formal optimization. Moreover, model performance was affected by staining quality and image resolution, and the lack of multicenter prospective validation limits its generalizability. Meanwhile, Andrade’s team built a real-time plasma cell detection model using You Only Look Once v7 (YOLOv7), achieving over 0.80 accuracy on public datasets, providing a viable solution for rapid screening in resource-limited environments [ 77 ]. Radiomics and automatic segmentation algorithms have demonstrated significant value in the quantitative assessment of MM lesions. Key research focuses include radiomics-based diagnostic models [ 78 , 79 ] and DL-driven automatic segmentation algorithms [ 80 – 82 ]. Sachpekidis et al. developed a DL-based automated workflow that achieved CT-based automatic segmentation of whole-body skeletal structures and mapped them to PET SUV maps, using multi-threshold strategies to calculate total tumor volume (MTV) and total lesion glycolysis (TLG) [ 82 ]. While this method provides an automated, reproducible quantitative approach to tumor burden assessment, its retrospective design and limited sample size limit direct clinical generalizability. Prognosis assessment and treatment decision Current prognostic stratification for MM relies on invasive bone marrow biopsies and cytogenetic analyses [ 83 ]. To address limited access to cytogenetic testing in real-world practice, Hao et al. developed a lumbar-spine MRI radiomics model for predicting ISS/R-ISS categories in newly diagnosed MM. A fusion model incorporating peripheral blood biomarkers further improved performance, suggesting a practical non-invasive supplement for baseline risk stratification when molecular profiling is unavailable [ 84 ]. The Mosquera team developed three molecular risk scores for MM by integrating clinical trial data from over 14,000 newly diagnosed cases (NDMM) into the EMN–HARMONY platform. These scores were validated against PFS and OS through internal cross-validation and external cohort testing, based on bone marrow cytogenetics and baseline clinical parameters [ 85 ]. In MM, risk stratification tools remain difficult to operationalize unless they align with established staging logic and real-world heterogeneity, because transplant eligibility, frailty, and extramedullary disease can shift outcomes independently of baseline biomarkers and may limit portability across treatment settings [ 86 ]. In recent years, the prognostic evaluation of MM has transitioned from static stratification to dynamic risk modeling. The SCOPE model, proposed by Hussain et al., is a time-series multi-task machine learning framework based on the transformer architecture. It can simultaneously perform event prediction, biomarker trajectory inference, and counterfactual treatment effect assessment [ 12 ]. Designed for NDMM and relapsed/refractory multiple myeloma (RRMM), this method provides time-updated, personalized, dynamic prognostic and treatment-selection criteria, demonstrating superior discrimination and calibration compared to static systems. However, its potential indicator bias and out-of-domain generalization capability remain unresolved challenges. The Murie team constructed the mmSYGNAL transcriptional program network based on Systems Genetic Network Analysis (SYGNAL), deriving program activity from multi-omics data and training cell genetics subtype-specific ML models accordingly, forming a unified framework for personalized dynamic risk assessment and treatment selection [ 87 ]. This framework performed excellently in multi-cohort external validation, with high consistency in drug response prediction and in vitro drug sensitivity. However, its clinical application still relies on high-quality omics data and prospective validation. Current AI research in MM has shifted focus from manual feature extraction to end-to-end frameworks. These systems utilize PET/CT and whole-body MRI for lesion detection, bone marrow burden quantification, and relapse prediction, while incorporating time-series models to transform prognosis assessment into an updatable dynamic system. Clinical challenges include inconsistent data acquisition and post-processing protocols, as well as insufficient prospective evidence. The next step involves establishing standardized imaging protocols and conducting multicenter clinical trials to validate their generalizability and clinical benefits. Representative studies in recent years are summarized in Table 3 . Table 3. Representative recent advances in artificial intelligence for MM Disease subtypes Objective Data type Model/Methodology Performance Strength Limitations Reference MM Automated smear-based MM cell identification Hematology smear images YOLOv7-based DNN Exactitude 84.0%; Completeness 80.0% Open dataset and code Lacks external validation [ 77 ] Automatic bone marrow ADC quantification for clinical assessment Whole body MRI ADC maps nnU-Net Dice right pelvis 0.92, left pelvis 0.93, sacrum 0.85 Multicenter training and testing; objective ADC measurement with reduced interreader variability Retrospective design; focus on pelvic marrow rather than whole skeleton [ 81 ] Prognostic risk stratification in NDMM Whole-body ¹¹C-MET and ¹⁸F-FDG PET/CT bone marrow images CT-guided 3D deep learning segmentation; SUV-based PET metabolic quantification PFS prediction AUC 0.74–0.75 for ¹¹C-MET MTV/TLMU Fully automated volumetric bone marrow assessment; dual-tracer comparison Dependence on ¹¹C-MET availability; no external validation [ 88 ] PCD-CT with DL denoising for MM lesion detection Photon-counting detector CT UHR images PCD-CT acquisition and CNN Higher spatial frequencies (≤ 1.98 vs. 0.99 mm⁻¹); Lower noise amplitude; Higher lesion conspicuity scores Higher conspicuity and detection at standard low dose; reader blinded design Lacks external validation [ 89 ] Cytogenetic risk classification for NDMM Whole body MRI plus spinal DCE and DWI RF, LASSO and ADASYN Best model AUC: 0.80; PR AUC: 0.79; sensitivity: 70.0%; specificity: 81.0% Noninvasive radiogenomic risk stratification Retrospective design; lacks external validation [ 90 ] Survival and therapy response prediction Gene expression microarray and RNA-seq and clinical ISS LASSO–Cox risk score (UPPRS); Cox nomogram UPPRS AUCs: Training AUC 0.71 (1-year)、0.92 (3-year); Validation AUC 0.68 (1-year)、0.66 (3- year)、0.64 (5-year) Dual cohort validation Retrospective public datasets [ 91 ] Open in a new tab Abbreviations: MM multiple myeloma, YOLO You Only Look Once, DNN deep neural network, MRI magnetic resonance imaging, ADC apparent diffusion coefficient, U-Net convolutional neural network architecture for biomedical image segmentation, nnU-Net no new net, EHR electronic health record, AUC area under the curve, PCD photon-counting detector, UHR ultra-high resolution, CNN convolutional neural network, NDMM newly diagnosed multiple myeloma, DCE dynamic contrast-enhanced magnetic resonance imaging, DWI diffusion-weighted imaging, RF random forest, LASSO least absolute shrinkage and selection operator, ADASYN adaptive synthetic sampling approach for imbalanced learning, AUPRC area under the precision–recall curve, UPPRS ubiquitin proteasome pathway risk score, ISS International Staging System Myelodysplastic syndromes Myelodysplastic syndromes (MDS) are myeloid clonal disorders characterized by hematopoietic stem cell dysfunction, dysplasia, and ineffective hematopoiesis [ 92 ]. In early MDS, increased apoptosis of bone marrow precursors is closely associated with ineffective hematopoiesis and contributes to peripheral cytopenias [ 93 ]. They exhibit diverse manifestations in peripheral blood and bone marrow, with a high risk of progression to AML [ 92 ]. Its diagnosis should be distinguished from other bone marrow failure disorders with overlapping clinical manifestations, particularly aplastic anemia (AA) and paroxysmal nocturnal hemoglobinuria (PNH), which are distinct entities but may similarly present with peripheral cytopenias [ 94 ]. This complexity poses a serious challenge to early diagnosis and precise stratification of MDS, but it also provides a broad space for AI applications. Diagnosis and disease classification Current AI-based diagnostic methods primarily rely on bone marrow smears, peripheral blood smears, and multi-parameter flow cytometry (MFC) data [ 95 ]. Among these, analysis of bone marrow smear images represents a key research direction. The cell classification algorithm for bone marrow smears developed by Lee et al. can automatically identify hematopoietic cell lineages in patients with MDS and detect abnormally growing cells in bone marrow smears. The AUC for distinguishing pathological and normal hematopoietic cells ranged from 0.945 to 0.996, and the accuracy ranged from 0.912 to 0.993, enabling automatic quantification of morphological characteristics [ 96 ]. In blood sample analysis, Zhu et al. optimized the MDS-CBC score (e-MDS-CBC) using ML by integrating the immature platelet fraction (IPF) with erythroid and granulocyte parameters, significantly improving MDS screening efficacy. The advantage lies in enabling primary screening with routine blood parameters alone. However, its generalizability depends on the distribution of white blood cell populations (CPD) and requires validation in broader populations [ 97 ]. Furthermore, AI models based on flow cytometry have demonstrated significant potential. The Clichet team developed an MFC-based MDS diagnostic model using the Elastic Net al.gorithm, identifying five key features from flow cytometry data. The model achieved an AUC of 0.935, 91.8% sensitivity, and 92.5% specificity on the validation set, outperforming the conventional Ogata score. However, its high data preprocessing requirements necessitate broader cohort validation for robustness [ 98 ]. Prognosis assessment and treatment decision The prognosis assessment for MDS has traditionally relied on scoring systems such as the International Prognostic Scoring System (IPSS) and its revised version, IPSS-R. The recently proposed molecular IPSS (IPSS-Mol) integrates the mutation status of 31 genes, achieving a discriminant C-index of 0.75 and significantly outperforming IPSS-R, emerging as a more valuable clinical tool [ 99 ]. Mosquera Orgueira et al. utilized the Rasch-Schwarz-Fischer (RSF) algorithm to incorporate eight basic clinical and cytogenetic variables, developing an MDS prognostic scoring system with C-indexes of 0.78 for OS and 0.84 for leukemia-free survival (LFS). Its external validation demonstrated comparable performance to IPSS-Mol without requiring genetic testing, though heterogeneity in data entry across centers may affect its long-term robustness [ 100 ]. Beyond static baseline scores, dynamic risk modeling using longitudinal clinical data is emerging in MDS. Bobak et al. developed a gradient-boosted decision-tree model that repeatedly estimates 1-year mortality risk using follow-up blood values plus diagnosis-based features, trained on the Düsseldorf MDS registry and externally validated in independent cohorts from Heidelberg and Mannheim, achieving AUROC values around 0.8 and outperforming a diagnosis-only baseline model [ 101 ]. In treatment decision-making, Radakovich et al. demonstrated that machine learning model based on continuous blood routine changes within 90 days after hypomethylating agent (HMA) therapy can predict treatment response early, providing a novel approach for cost-effective dynamic monitoring. However, current evidence remains limited to retrospective single-center validation [ 102 ]. Allogeneic hematopoietic stem cell transplantation (Allo-HSCT) remains the only potentially curative approach for MDS [ 103 ]. The selection of pre-transplant conditioning regimens is critical. Shimomura’s team developed a machine learning model using 2,567 transplantation cases that could distinguish patient populations that benefited from myeloablative conditioning (MAC) from those that benefited from reduced-intensity conditioning (RIC). This data-driven support for personalized transplantation strategies highlights the value of AI in complex treatment decisions, though its conclusions require confirmation through prospective multicenter studies [ 104 ]. Current AI applications in MDS primarily focus on two domains: streamlined multi-parameter flow models and deep learning for bone marrow smears. The prognostic aspect is transitioning from static baseline analysis to data fusion and temporalization. However, its widespread adoption faces challenges, including noise in labeled data, difficulty distinguishing critical disease cases, limited sample sizes, and model generalization issues due to data domain shifts. We have summarized representative related studies in recent years, as shown in Table 4 . Table 4. Representative recent advances in artificial intelligence for MDS Disease subtypes Objective Data type Model/Methodology Performance Strength Limitations Reference MDS Automated bone-marrow smear cell classification and dysplasia detection for MDS diagnosis Bone marrow aspiration images U-Net; Inception V3; Grad-CAM Accuracy: 91.0%–99.0%; cell classification AUC: 0.94–0.99 Public dataset and code; heatmap-based interpretability Lacks external validation [ 96 ] MFC-based AI diagnosis of MDS Ten color single tube MFC parameters Elasticnet al.gorithm; Boruta algorithm External AUC: 0.935; sensitivity above 91.0%;specificity above 92.0% Multicenter study Instrument brand variability [ 98 ] Capturing complex interactions to enhance prognostic discrimination Clinical labs, marrow morphology and cytogenetics (baseline, 18 variables) RF, eight variable model C-index (OS): train 0.76; test 0.78 Large national registry; external validation Retrospective design [ 100 ] Early prediction of HMA response from 0–90 d CBC CBC time series (0–90 d) RF, XGBoost and LightGBM XGBoost (final adoption): AUROC 0.79 (test)/0.82 (external); PR-AUC 0.78 (test)/0.84 (external) Uses routine labs only; interpretable SHAP Retrospective design [ 102 ] Automated neutrophil-dysplasia recognition for MDS screening Peripheral blood smear images U-Net, EfficientNetV2 and SAM-2 Cell level accuracy: 94.0%, sensitivity: 95.0%, specificity: 94%; patient level sensitivity: 98.0%, specificity: 96.0%, AUC: 0.999 Neutrophil only morphology no human crafted features Lacks external validation [ 105 ] Computational cytomorphology detection from PB smears Peripheral blood smear images DenseNet-121,U-Net, XGBoost and Elasticnet al.gorithm MDS genetic subtyping: AUC above 0.89 Open source pipeline and data assets Predefined features for parts of pipeline; Retrospective design [ 106 ] Open in a new tab Abbreviations: MDS myelodysplastic syndromes, U-Net convolutional neural network architecture for biomedical image segmentation, AUC area under the curve, MFC multiparameter flow cytometry, RF random forest, HMA hypomethylating agents, CBC complete blood count, SHAP Shapley additive explanations, SAM Segment Anything Model Challenges and strategies in clinical translation While AI research in hematologic malignancies continues to gain momentum, its transition from algorithmic innovation to clinical implementation faces significant challenges. To transform models that excel in retrospective data into reliable real-world decision-support tools, we must systematically bridge the gaps in data, algorithms, and ethical oversight (Fig. 2 ). Fig. 2. Open in a new tab Challenges and response strategies in AI integration for hematologic malignancies. This diagram illustrates the key challenges and corresponding response strategies in the integration and application of AI in the field of hematologic malignancies. At the center, it covers critical challenges such as “Ethics and regulations,” “Algorithmic limitations,” and “Data integration and use.” Surrounding these core challenges are the corresponding response strategies. These elements represent the key considerations for advancing the responsible and effective use of AI in the field of hematologic malignancies. This figure was created with Adobe Illustrator Data aspects High-quality, large-scale training data is fundamental to ensuring AI’s strong generalization capabilities [ 15 ]. In hematologic malignancies, dataset complexity extends beyond sample size: clinically meaningful labels are hierarchical and periodically revised, which can complicate retrospective harmonization across cohorts [ 39 , 107 , 108 ]. Moreover, clinically relevant endpoints are often time-varying and assay-dependent, increasing the need for standardized reporting and improved cross-platform harmonization, with broader multicenter external validation still needed [ 109 , 110 ]. Several open or access-controlled resources now support benchmarking and validation, including CRDC/GDC [ 111 ], MMRF CoMMpass [ 112 ], TARGET [ 113 ], Beat AML [ 114 ], St. Jude Cloud [ 115 ], FlowRepository [ 116 ], and EGA [ 117 ], while Scientific Data [ 118 ] and Zenodo [ 119 ] are expanding reusable cytomorphology and cytometry benchmarks. However, the inherent multimodal nature of hematologic tumor data and clinical realities pose a primary challenge. Diagnostic data—spanning morphology, immunology, cytogenetics, and molecular biology—vary not only in origin and format but are also scattered across heterogeneous information systems across different medical institutions, creating severe “data silos” [ 120 ]. Furthermore, differences in sample processing, staining protocols, and scanning equipment among centers introduce uncontrollable biases, severely limiting the models’ out-of-domain generalization performance [ 16 ]. To overcome data bottlenecks and achieve clinically actionable outcomes, a phased approach of “data construction—standardization—integration” should be implemented. The first step involves establishing a multi-center shared database that systematically aggregates multimodal raw data (e.g., morphological and imaging) while simultaneously recording interoperable metadata [ 18 ]. This metadata facilitates the identification of domain and time drift and provides structured support for external validation and generalization evaluation. Secondly, establishing unified data collection and quality control standards can reduce technical variations at the source [ 19 ], improve cross-center consistency and comparability, and emphasize practicality and implementability. Finally, promote multimodal alignment and hierarchical fusion [ 121 ], adopt traceable, interpretable fusion strategies, and seamlessly integrate with EHR workflows. This measure aims to systematically reduce data heterogeneity, enhance the model’s robustness and out-of-domain generalization in a multi-center environment, and support continuous drift monitoring and model governance. Algorithmic aspects In algorithmic development, three core challenges persist: generalization, interpretability, and fairness. First, inadequate generalization capability remains a critical barrier to clinical translation. Models trained on specific datasets often exhibit “domain shift” phenomena, making them ill-suited for diverse populations, testing platforms, or evolving clinical practices. This directly results in significant performance degradation of AI tools during cross-center validation [ 17 , 19 ]. Second, the “black box” decision-making inherent in complex models like deep learning severely hinders clinical adoption. The opaque decision-making processes make it difficult for physicians to understand and trust model outputs, creating a fundamental obstacle in life-critical medical decisions [ 122 ]. Third, algorithmic bias and fairness issues demand attention. When training data inadequately represent diverse racial, age, and gender groups, models may amplify existing healthcare inequalities, leading to systematic diagnostic or prognostic biases against minority populations [ 123 ]. To address algorithmic challenges, a self-supervised pre-training and task-adaptive architecture can be adopted. This framework first constructs a general representation with strong generalization capability through cross-modal alignment and contrastive learning using large-scale unlabeled multimodal medical data [ 124 ]. Subsequently, it performs fine-tuning on downstream tasks with limited labeled samples to adapt to scenarios such as diagnostic classification and prognosis evaluation [ 125 , 126 ]. By incorporating causal annotations and expert prior knowledge, the model’s decision-making becomes more interpretable and clinically credible at the mechanistic level. Its output also extends from traditional classification probability to event risk and individualized intervention benefit, aligning more closely with the logic of clinical decision-making [ 126 ] (Fig. 3 ). Fig. 3. Open in a new tab A framework for self-supervision and task adaptation with causal annotation and expert priors. ( A ) Data acquisition and governance: Genomics, histopathology, radiomics, etc., are integrated into a unified “multimodal medical data” layer with real-time ingestion, standardization, and de-identification/quality control to enable compliant downstream learning. ( B ) Multimodal self-supervised learning: Cross-modal alignment harmonizes heterogeneous feature matrices across modalities. Concretely, correspondence is enforced at the patient level (row wise): modality specific matrices are mapped by projection heads into a shared latent space, and no column wise (feature to feature) matching is assumed. In this schematic, canonical correlation analysis (CCA) is used as an illustrative alignment objective, although contrastive (InfoNCE) losses could also be used. Contrastive learning uses positive pairs (from the same sample) and negative pairs (from different samples) through a shared weight encoder to learn transferable representations; causal annotation generates counterfactual rationales and a Δprediction = f(x) − f(x′) effect for interpretability. ( C ) Foundation models and task adaptation: After data preprocessing, foundation models are pretrained with a forecasting objective and then fine-tuned for clinical event prediction. ( D ) Model outputs: The system returns calibrated probabilities, estimates of time to event risk, and an individual causal lift score to support risk-informed clinical decisions. This figure was created with Adobe Illustrator However, the introduction of such complex models also makes their robustness and reliability critical prerequisites for clinical translation. Therefore, rigorous validation and interpretive techniques must be implemented. First, models must undergo large-scale, multicenter external validation and adhere strictly to the TRIPOD + AI Declaration for complete and transparent reporting [ 15 ]. Second, explainable AI (XAI) techniques such as SHAP, LIME, and attention mechanisms should be integrated to visualize the decision-making basis of models, transforming them from “black boxes” into “transparent systems” [ 122 ]. Finally, fairness audits throughout the entire model development cycle—actively detecting and correcting performance disparities across subgroups—are ethically imperative to ensure health equity [ 127 ]. These measures aim to enhance AI model performance, enabling them to better serve clinical practice. Ethical and regulatory aspects The clinical deployment of AI must ensure patient safety and public welfare, yet this process faces multiple challenges. The foremost issue lies in liability delineation and accountability. When AI-assisted decision-making errors occur, the legal responsibility among developers, deployment institutions, and clinicians remains an unresolved gray area [ 124 ]. Second, regulatory science faces challenges. The traditional medical device approval process struggles to keep pace with the rapid iteration of AI software. Regulatory agencies need to establish a more adaptive review framework to balance innovation, safety, and efficiency [ 103 ]. Additionally, broad social and ethical risks cannot be overlooked. Beyond algorithmic bias, issues such as data misuse and patient consent must be proactively considered [ 123 , 128 , 129 ]. To address ethical and regulatory challenges and establish a trustworthy clinical AI system, systematic progress must be made through institutional design and technological synergy. At the institutional level, promoting transparent research reporting and trial pre-registration forms the foundation of trust. Transparent reporting requires full disclosure of the entire research process, in accordance with international standards such as TRIPOD + AI [ 15 ], ensuring procedural transparency and reproducible results. Trial pre-registration helps clarify research hypotheses and evaluation metrics, preventing selection bias at the source and enhancing evidentiary reliability [ 106 ]. Regarding technological implementation, advocating human-machine collaborative evaluation and decision-making paradigms is essential [ 17 ]. It is crucial to define the primary role in critical decisions alongside AI’s supportive function, ensuring AI outputs effectively assist rather than interfere with clinical judgment. Future directions and summary In the future, the diagnosis and treatment of hematologic cancers may increasingly move toward multi-level, multimodal data integration and more coordinated AI-assisted decision support to enable dynamic optimization of clinical care. Based on the AI clinical deployment process, this system can be divided into several tiers: the infrastructure and input layer, the model processing layer, the analysis layer, and the decision-making and evaluation layer, corresponding to the information flow shown in Fig. 4 . Fig. 4. Open in a new tab Framework for clinical AI deployment: from infrastructure to analysis and feedback. Infrastructure and input layer: Multimodal medical data (genomics, histopathology, radiomics) are ingested in real time, standardized, and de-identified under data governance; resource inputs include clinical instrumentation, therapeutics, and human resources, building foundational capabilities for clinical AI deployment. Processing layer: A multimodal learning layer performs feature alignment across heterogeneous modalities with contrastive and self-supervised learning; a prognostic risk-assessment module supports diagnostic classification and prognosis using deep learning and multitask learning; a personalized-treatment layer enables risk-adapted selection of chemotherapy, targeted therapy, immunotherapy, or cellular therapy. Analysis layer: Model outputs are converted into analyses and feedback (e.g., response rate plots and longitudinal benefit indices), and these are combined with a cost benefit analysis layer to support value conscious decision making. Decision and evaluation layer: Multicontext assessment (safety, economic value, patient experience, model performance) informs scenario optimization, including technical optimization (post-deployment isotonic calibration with rolling-window updates; ingestion of longitudinal data to refresh risk estimates) and structural optimization (model governance, drift monitoring with versioning and audit trails, and ethical/regulatory compliance). This figure was created with Adobe Illustrator The realization of this vision depends on the deep integration of AI with EHR systems. For instance, generative models like Foresight have demonstrated the feasibility of building universal temporal models based on large-scale real-world EHRs [ 130 ]. In such systems, AI could function as a monitoring and decision-support layer that helps identify evolving patient risks and trigger clinician review when necessary. These alerts are then reviewed by physicians to guide interventions, thereby driving the automation and dynamism of the diagnostic and treatment process (Fig. 5 ). Fig. 5. Open in a new tab AI decision support loop embedded in the EHR. An AI decision support loop embedded in the EHR ingests streaming inputs (latest laboratory results, vital signs, medications and physician orders) and returns outputs for clinical action: risk stratification, initiated consultation, recommendation dialog and second confirmation step to ensure safety with human oversight. This figure was created using BioRender AI has shown potential in hematologic malignancy diagnosis and treatment, with emerging applications spanning disease diagnosis and classification, prognosis assessment, and treatment decision support. To realize this vision clinically, rigorous validation, regulatory approvals, robust IT infrastructure, and ongoing professional oversight are essential. This review has several limitations. It is a narrative rather than a systematic review, and selection bias cannot be excluded. The included studies are highly heterogeneous in disease subtype, data modality, study design, and evaluation metrics, limiting direct comparison across studies. Many cited AI studies remain retrospective, single-center, or proof-of-concept, with limited prospective validation and uncertain generalizability. In addition, improved model performance does not necessarily translate into clinical utility or patient benefit. Discussions on LLM-assisted decision support and EHR-embedded closed-loop systems are forward-looking and should be interpreted as conceptual directions rather than established standards of care. While challenges remain, continued methodological refinement, accumulation of higher-quality clinical evidence, and deeper interdisciplinary collaboration may expand the role of AI in hematologic malignancies. Such progress may support more dynamic and individualized medical care, but its clinical translation will still require rigorous validation, governance, and responsible implementation. Acknowledgements The authors thank their colleagues for constructive comments and technical support. Author contributions Z.L., J.Z., L.H., and X.C. conducted the literature search and study screening, extracted and synthesized the evidence, and drafted the manuscript. J.W. and W.T. conceived the scope and structure of the review, provided supervision and critical revisions for important intellectual content, and finalized the manuscript. All authors reviewed and approved the final version for submission. Funding This work was supported by the Fundamental Research Program of Shanxi Province (Grant No. 202303021211224; Weiwei Tian) and the Open Fund of the Key Laboratory of Molecular Diagnosis and Treatment of Hematological Diseases of Shanxi Province (Grant No. KLMDT202402; Weiwei Tian). 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