ReviewJournal of medical Internet research2022
A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects.
Review in Journal of medical Internet research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 1 of them a synthesis that pooled it.
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Who cites it
31 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook.Frontiers in oncology · 2025Pooled it
- Review
- Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records.Cancers · 2026Article
- Artificial intelligence reshaping the paradigm of hematologic malignancy diagnosis and treatment: From static assessment to dynamic precision management.Annals of hematology · 2026Review
- Explainable federated transformer framework for joint leukemia classification and stage prediction.Scientific reports · 2026Article
- Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification.Frontiers in artificial intelligence · 2026Article
- Innovative hematological strategies to combat maternal and child mortality in Africa: focus on anemia and sickle cell disease.Annals of medicine and surgery (2012) · 2026Review
- AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia.Biomedical engineering and computational biology · 2026Article
- A personalized communication efficient federated learning framework with low rank adaptation for intelligent leukemia diagnosis.Scientific reports · 2025Article
- Application of artificial intelligence in laboratory hematology: Advances, challenges, and prospects.Acta pharmaceutica Sinica. B · 2025Review
- Machine learning improves detection of alpha thalassemia carriers compared to clinical features.Scientific reports · 2025Article
- Current scenario of allogeneic hematopoietic stem cell transplantation in adult patients with relapsed/refractory acute myeloid leukemia: a narrative review.Annals of hematology · 2025Review
- Neuro-Bridge-X: A Neuro-Symbolic Vision Transformer with Meta-XAI for Interpretable Leukemia Diagnosis from Peripheral Blood Smears.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.International journal of molecular sciences · 2025Review
- AI-Powered Spectral Imaging for Virtual Pathology Staining.Bioengineering (Basel, Switzerland) · 2025Article
- Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.Annals of medicine and surgery (2012) · 2025Review
- Artificial Intelligence (AI) and Drug-Induced and Idiosyncratic Cytopenia: The Role of AI in Prevention, Prediction, and Patient Participation.Hematology reports · 2025Review
- Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment.Biomedicines · 2025Review
- Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study.Journal of medical Internet research · 2025Article
- Are we ready to integrate advanced artificial intelligence models in clinical laboratory?Biochemia medica · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMachine learning (ML) and deep learning (DL) methods have recently garnered a great deal of attention in the field of cancer research by making a noticeable contribution to the growth of predictive medicine and modern oncological practices. Considerable focus has been particularly directed toward hematologic malignancies because of the complexity in detecting early symptoms. Many patients with blood cancer do not get properly diagnosed until their cancer has reached an advanced stage with limited treatment prospects. Hence, the state-of-the-art revolves around the latest artificial intelligence (AI) applications in hematology management.
objectiveThis comprehensive review provides an in-depth analysis of the current AI practices in the field of hematology. Our objective is to explore the ML and DL applications in blood cancer research, with a special focus on the type of hematologic malignancies and the patient's cancer stage to determine future research directions in blood cancer.
methodsWe searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number of keywords. We included studies written in English and published between 2015 and 2021. For each study, we identified the ML and DL techniques used and highlighted the performance of each model.
resultsUsing the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review.
conclusionsThe current literature suggests that the application of AI in the field of hematology has generated impressive results in the screening, diagnosis, and treatment stages. Nevertheless, optimizing the patient's pathway to treatment requires a prior prediction of the malignancy based on the patient's symptoms or blood records, which is an area that has still not been properly investigated.
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