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A Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/9781119792611.ch14openalex-snapshot

Early Stage Detection of Leukemia Using Artificial Intelligence

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Validation of the acute leukemia‐EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi‐center GITMO cohort

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The LEukemia Artificial Intelligence Program (LEAP) in chronic myeloid leukemia in chronic phase: A model to improve patient outcomes

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An artificial intelligence‐assisted diagnostic platform for rapid near‐patient hematology

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Patient‐based prediction algorithm of relapse after allo‐HSCT for acute Leukemia and its usefulness in the decision‐making process using a machine learning approach

inferredcites →https://doi.org/10.1002/cncr.32344openalex-snapshot

Individualized prediction of leukemia‐free survival after autologous stem cell transplantation in acute myeloid leukemia

inferredcites →https://doi.org/10.1002/cyto.a.23852openalex-snapshot

Automated Flow Cytometric MRD Assessment in Childhood Acute B‐ Lymphoblastic Leukemia Using Supervised Machine Learning

inferredcites →https://doi.org/10.1002/cyto.a.24159openalex-snapshot

Hematologist‐Level Classification of Mature B‐Cell Neoplasm Using Deep Learning on Multiparameter Flow Cytometry Data

inferredcites →https://doi.org/10.1002/jemt.23509openalex-snapshot

Accurate automatic detection of acute lymphatic leukemia using a refined simple classification

inferredcites →https://doi.org/10.1007/978-3-030-00665-5_163openalex-snapshot

Review on Image Segmentation Techniques Incorporated with Machine Learning in the Scrutinization of Leukemic Microscopic Stained Blood Smear Images

inferredcites →https://doi.org/10.1007/978-3-030-73603-3_35openalex-snapshot

Optimising Criteria for Manual Smear Review Following Automated Blood Count Analysis: A Machine Learning Approach

inferredcites →https://doi.org/10.1007/978-3-319-99133-7_12openalex-snapshot

Classifying Leukemia and Gout Patients with Neural Networks

inferredcites →https://doi.org/10.1007/978-981-15-0798-4openalex-snapshot

ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging

inferredcites →https://doi.org/10.1007/978-981-15-3689-2_8openalex-snapshot

Computer Vision and Machine Learning Approach for Malaria Diagnosis in Thin Blood Smears from Microscopic Blood Images

inferredcites →https://doi.org/10.1007/978-981-15-6067-5openalex-snapshot

Innovations in Computational Intelligence and Computer Vision

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Computer Assisted Classification Framework for Detection of Acute Myeloid Leukemia in Peripheral Blood Smear Images

inferredcites →https://doi.org/10.1007/978-981-15-9671-1_17openalex-snapshot

Advanced Machine Learning for Leukaemia Detection Based on White Blood Cell Segmentation

inferredcites →https://doi.org/10.1007/978-981-33-4073-2_17openalex-snapshot

Acute-Lymphoblastic Leukemia Detection Through Deep Transfer Learning Approach of Neural Network

inferredcites →https://doi.org/10.1007/978-981-33-4673-4_19openalex-snapshot

An Effective Leukemia Prediction Technique Using Supervised Machine Learning Classification Algorithm

inferredcites →https://doi.org/10.1007/s00500-019-04383-8openalex-snapshot

A fused CNN model for WBC detection with MRMR feature selection and extreme learning machine

inferredcites →https://doi.org/10.1007/s10278-018-0074-yopenalex-snapshot

An Automatic and Robust Decision Support System for Accurate Acute Leukemia Diagnosis from Blood Microscopic Images

inferredcites →https://doi.org/10.1007/s10527-021-10038-6openalex-snapshot

Artificial Intelligence Technologies in the Diagnosis of Acute Lymphoblastic Leukemia and Minimal Residual Disease

inferredcites →https://doi.org/10.1007/s10916-018-0972-zopenalex-snapshot

Proteomics Versus Clinical Data and Stochastic Local Search Based Feature Selection for Acute Myeloid Leukemia Patients’ Classification

inferredcites →https://doi.org/10.1007/s11517-020-02245-2openalex-snapshot

Identification of significant risks in pediatric acute lymphoblastic leukemia (ALL) through machine learning (ML) approach

inferredcites →https://doi.org/10.1007/s13721-017-0146-9openalex-snapshot

Toward leveraging big value from data: chronic lymphocytic leukemia cell classification

inferredcites →https://doi.org/10.1016/j.aej.2017.08.025openalex-snapshot

A decision support system for Acute Leukaemia classification based on digital microscopic images

inferredcites →https://doi.org/10.1016/j.asoc.2017.03.024openalex-snapshot

Intelligent leukaemia diagnosis with bare-bones PSO based feature optimization

inferredcites →https://doi.org/10.1016/j.beha.2020.101192openalex-snapshot

Acute myeloid leukemia and artificial intelligence, algorithms and new scores

inferredcites →https://doi.org/10.1016/j.cmpb.2017.09.011openalex-snapshot

A segmentation method based on HMRF for the aided diagnosis of acute myeloid leukemia

inferredcites →https://doi.org/10.1016/j.cmpb.2019.06.014openalex-snapshot

A decision-tree approach for the differential diagnosis of chronic lymphoid leukemias and peripheral B-cell lymphomas

inferredcites →https://doi.org/10.1016/j.cmpb.2021.105999openalex-snapshot

A deep learning model (ALNet) for the diagnosis of acute leukaemia lineage using peripheral blood cell images

inferredcites →https://doi.org/10.1016/j.ebiom.2018.10.042openalex-snapshot

Clinically validated machine learning algorithm for detecting residual diseases with multicolor flow cytometry analysis in acute myeloid leukemia and myelodysplastic syndrome

inferredcites →https://doi.org/10.1016/j.exphem.2019.08.009openalex-snapshot

Machine learning predicts putative hematopoietic stem cells within large single-cell transcriptomics data sets

inferredcites →https://doi.org/10.1016/j.isci.2019.100780openalex-snapshot

Scalable Prediction of Acute Myeloid Leukemia Using High-Dimensional Machine Learning and Blood Transcriptomics

inferredcites →https://doi.org/10.1016/j.pathol.2020.12.004openalex-snapshot

Machine learning and augmented human intelligence use in histomorphology for haematolymphoid disorders

inferredcites →https://doi.org/10.1016/j.tranon.2019.09.009openalex-snapshot

Cell Population Data–Driven Acute Promyelocytic Leukemia Flagging Through Artificial Neural Network Predictive Modeling

inferredcites →https://doi.org/10.1016/j.transci.2020.102986openalex-snapshot

Artificial intelligence meets hematology

inferredcites →https://doi.org/10.1021/acssensors.0c01811openalex-snapshot

Identification and Staging of B-Cell Acute Lymphoblastic Leukemia Using Quantitative Phase Imaging and Machine Learning

inferredcites →https://doi.org/10.1038/s41374-020-0442-3openalex-snapshot

Deep learning shows the capability of high-level computer-aided diagnosis in malignant lymphoma

inferredcites →https://doi.org/10.1038/s41375-019-0387-yopenalex-snapshot

Data mining for mutation-specific targets in acute myeloid leukemia

inferredcites →https://doi.org/10.1038/s41467-017-02465-5openalex-snapshot

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

inferredcites →https://doi.org/10.1038/s41467-019-14225-8openalex-snapshot

Machine learning can identify newly diagnosed patients with CLL at high risk of infection

inferredcites →https://doi.org/10.1038/s41467-020-19817-3openalex-snapshot

A deep learning diagnostic platform for diffuse large B-cell lymphoma with high accuracy across multiple hospitals

inferredcites →https://doi.org/10.1038/s41598-017-07408-0openalex-snapshot

Machine learning applications for prediction of relapse in childhood acute lymphoblastic leukemia

inferredcites →https://doi.org/10.1038/s41598-017-18564-8openalex-snapshot

An application of machine learning to haematological diagnosis

inferredcites →https://doi.org/10.1038/s41598-020-59215-9openalex-snapshot

Efficient Classification of White Blood Cell Leukemia with Improved Swarm Optimization of Deep Features

inferredcites →https://doi.org/10.1038/s41598-020-61247-0openalex-snapshot

Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

inferredcites →https://doi.org/10.1038/s41598-020-67559-5openalex-snapshot

Author Correction: Artificial Intelligence based Models for Screening of Hematologic Malignancies using Cell Population Data

inferredcites →https://doi.org/10.1038/s41598-020-78315-0openalex-snapshot

Systems biology drug screening identifies statins as enhancers of current therapies in chronic lymphocytic leukemia

inferredcites →https://doi.org/10.1074/mcp.m115.050245openalex-snapshot

Machine Learning-based Classification of Diffuse Large B-cell Lymphoma Patients by Their Protein Expression Profiles

inferredcites →https://doi.org/10.1080/17474086.2019.1660159openalex-snapshot

Remaining challenges in predicting patient outcomes for diffuse large B-cell lymphoma

inferredcites →https://doi.org/10.1080/23723556.2018.1472057openalex-snapshot

Single-cell mass cytometry and machine learning predict relapse in childhood leukemia

inferredcites →https://doi.org/10.1088/1742-6596/1725/1/012012openalex-snapshot

Multiclass classification of leukemia cancer data using Fuzzy Support Vector Machine (FSVM) with feature selection using Principal Component Analysis (PCA)

inferredcites →https://doi.org/10.1093/ajcp/aqaa166openalex-snapshot

Augmented Human Intelligence and Automated Diagnosis in Flow Cytometry for Hematologic Malignancies

inferredcites →https://doi.org/10.1093/ajcp/aqab086openalex-snapshot

A Machine Learning Model to Successfully Predict Future Diagnosis of Chronic Myelogenous Leukemia With Retrospective Electronic Health Records Data

inferredcites →https://doi.org/10.1093/jamia/ocaa060openalex-snapshot

Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia

inferredcites →https://doi.org/10.1109/access.2019.2946240openalex-snapshot

Early Imaging-Based Predictive Modeling of Cognitive Performance Following Therapy for Childhood ALL

inferredcites →https://doi.org/10.1109/access.2020.3012292openalex-snapshot

Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/access.2020.3048172openalex-snapshot

A Review on Traditional Machine Learning and Deep Learning Models for WBCs Classification in Blood Smear Images

inferredcites →https://doi.org/10.1109/asicon.2017.8252657openalex-snapshot

Automatic classification of leukocytes using deep neural network

inferredcites →https://doi.org/10.1109/bibm49941.2020.9313319openalex-snapshot

Classification of Single Cell Types During Leukemia Therapy using Artificial Neural Networks

inferredcites →https://doi.org/10.1109/ccaa.2016.7813896openalex-snapshot

A review to detect leukemia cancer in medical images

inferredcites →https://doi.org/10.1109/cesys.2018.8724075openalex-snapshot

Diagnosis of Leukemia and its types Using Digital Image Processing Techniques

inferredcites →https://doi.org/10.1109/codit.2019.8820299openalex-snapshot

Automatic Acute Lymphoblastic Leukemia Detection and Comparative Analysis from Images

inferredcites →https://doi.org/10.1109/fcv.2015.7103739openalex-snapshot

Acute lymphoid leukemia classification using two-step neural network classifier

inferredcites →https://doi.org/10.1109/gucon48875.2020.9231134openalex-snapshot

Blood Cells Detection Using Faster-RCNN

inferredcites →https://doi.org/10.1109/i-smac47947.2019.9032639openalex-snapshot

Recognizing Presence of Hematological Disease using Deep Learning

inferredcites →https://doi.org/10.1109/ic3iot.2018.8668141openalex-snapshot

Recognition and Arrangement of Blood Cancer from Microscopic Cell Pictures Utilizing Support Vector Machine K-Nearest Neighbor and Deep Learning

inferredcites →https://doi.org/10.1109/icacci.2018.8554576openalex-snapshot

Identification of Acute Lymphoblastic Leukemia in Microscopic Blood Image Using Image Processing and Machine Learning Algorithms

inferredcites →https://doi.org/10.1109/icasert.2019.8934628openalex-snapshot

Genetic Expression Analysis To Detect Type Of Leukemia Using Machine Learning

inferredcites →https://doi.org/10.1109/iccia.2018.00036openalex-snapshot

Using Convolutional Neural Networks for Automated Fine Grained Image Classification of Acute Lymphoblastic Leukemia

inferredcites →https://doi.org/10.1109/icece51571.2020.9393069openalex-snapshot

Prediction and Rule Generation for Leukemia using Decision Tree and Association Rule Mining

inferredcites →https://doi.org/10.1109/iciet48527.2019.9290544openalex-snapshot

A Comprehensive Study of Machine Learning algorithms for Predicting Leukemia Based on Biomedical Data

inferredcites →https://doi.org/10.1109/icitech.2017.8079917openalex-snapshot

Blood diseases detection using data mining techniques

inferredcites →https://doi.org/10.1109/icoei.2017.8300983openalex-snapshot

Detection of leukemia and its types using image processing and machine learning

inferredcites →https://doi.org/10.1109/icpc2t48082.2020.9071471openalex-snapshot

Leukemia Disease Detection and Classification Using Machine Learning Approaches: A Review

inferredcites →https://doi.org/10.1109/icter.2018.8615472openalex-snapshot

Machine Learning Approach to Predict the Survival Time of Childhood Acute Lymphoblastic Leukemia Patients

inferredcites →https://doi.org/10.1109/ictke.2018.8612303openalex-snapshot

Leukemia Prediction from Microscopic Images of Human Blood Cell Using HOG Feature Descriptor and Logistic Regression

inferredcites →https://doi.org/10.1109/indicon47234.2019.9029034openalex-snapshot

Machine Learning based System for Automatic Detection of Leukemia Cancer Cell

inferredcites →https://doi.org/10.1109/inocon50539.2020.9298321openalex-snapshot

Leukemia Diagnosis Based on Machine Learning Algorithms

inferredcites →https://doi.org/10.1109/itt48889.2019.9075067openalex-snapshot

Predicting Survivability in Leukemia Patients using Deep Learning

inferredcites →https://doi.org/10.1109/mecbme47393.2020.9265154openalex-snapshot

White Blood Cells Classification Using Convolutional Neural Network Hybrid System

inferredcites →https://doi.org/10.1109/rait.2018.8389068openalex-snapshot

Phase classification of chronic myeloid leukemia using convolution neural networks

inferredcites →https://doi.org/10.1109/rbme.2020.3004639openalex-snapshot

Segmentation of White Blood Cell, Nucleus and Cytoplasm in Digital Haematology Microscope Images: A Review–Challenges, Current and Future Potential Techniques

inferredcites →https://doi.org/10.1109/tem.2021.3103549openalex-snapshot

Peripheral Blood Smear Analysis Using Automated Computer-Aided Diagnosis System to Identify Acute Myeloid Leukemia

inferredcites →https://doi.org/10.1109/tencon50793.2020.9293925openalex-snapshot

Leukemia Detection Mechanism through Microscopic Image and ML Techniques

inferredcites →https://doi.org/10.1111/bjh.15774openalex-snapshot

Artificial intelligence and machine learning in haematology

inferredcites →https://doi.org/10.1111/ijlh.12818openalex-snapshot

Image processing and machine learning in the morphological analysis of blood cells

inferredcites →https://doi.org/10.1111/ijlh.13042openalex-snapshot

Digital morphology analyzers in hematology: ICSH review and recommendations

inferredcites →https://doi.org/10.1111/ijlh.13089openalex-snapshot

Machine learning applications in the diagnosis of leukemia: Current trends and future directions

inferredcites →https://doi.org/10.1136/jclinpath-2019-205949openalex-snapshot

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis

inferredcites →https://doi.org/10.1145/3297001.3297019openalex-snapshot

Towards Designing an Automated Classification of Lymphoma subtypes using Deep Neural Networks

inferredcites →https://doi.org/10.1155/2017/3587309openalex-snapshot

Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System

inferredcites →https://doi.org/10.1155/2019/7519603openalex-snapshot

Convolutional Neural Networks for Recognition of Lymphoblast Cell Images

inferredcites →https://doi.org/10.1155/2020/6648574openalex-snapshot

IoMT-Based Automated Detection and Classification of Leukemia Using Deep Learning

inferredcites →https://doi.org/10.1155/2021/5478157openalex-snapshot

Timely Diagnosis of Acute Lymphoblastic Leukemia Using Artificial Intelligence‐Oriented Deep Learning Methods

inferredcites →https://doi.org/10.1158/2643-3230.bcd-21-0048openalex-snapshot

Toward Artificial Intelligence–Driven Pathology Assessment for Hematologic Malignancies

inferredcites →https://doi.org/10.11591/ijece.v5i3.pp483-490openalex-snapshot

Partial Context Similarity of Gene/Proteins in Leukemia Using Context Rank Based Hierarchical Clustering Algorithm

inferredcites →https://doi.org/10.1177/0954411920938567openalex-snapshot

Design of an integrated model for diagnosis and classification of pediatric acute leukemia using machine learning

inferredcites →https://doi.org/10.1177/1176935119835544openalex-snapshot

Predicting Complete Remission of Acute Myeloid Leukemia: Machine Learning Applied to Gene Expression

inferredcites →https://doi.org/10.1177/1533033818802789openalex-snapshot

Acute Lymphoblastic Leukemia Detection and Classification of Its Subtypes Using Pretrained Deep Convolutional Neural Networks

inferredcites →https://doi.org/10.1182/blood-2012-06-435040openalex-snapshot

WT1-specific T-cell responses in high-risk multiple myeloma patients undergoing allogeneic T cell–depleted hematopoietic stem cell transplantation and donor lymphocyte infusions

inferredcites →https://doi.org/10.1182/blood-2018-99-114794openalex-snapshot

Prediction of Acute Graft-Versus-Host Disease Following Allogeneic Hematopoietic Stem Cell Transplantation Using a Machine Learning Algorithm

inferredcites →https://doi.org/10.1182/blood-2018-99-116685openalex-snapshot

Pan-Myeloid Leukemia Analysis: Machine Learning-Based Approach to Predict Phenotype and Clinical Outcomes Using Mutation Data

inferredcites →https://doi.org/10.1182/blood-2019-128066openalex-snapshot

A Personalized Prediction Model to Risk Stratify Patients with Acute Myeloid Leukemia (AML) Using Artificial Intelligence

inferredcites →https://doi.org/10.1182/blood-2019-128243openalex-snapshot

Use of Machine Learning in 2074 Cases of Acute Myeloid Leukemia for Genetic Risk Profiling

inferredcites →https://doi.org/10.1182/blood-2019-128823openalex-snapshot

Supervised Machine Learning Algorithms Using Patient Related Factors to Predict in-Hospital Mortality Following Acute Myeloid Leukemia Therapy

inferredcites →https://doi.org/10.1182/blood-2019-129432openalex-snapshot

Machine Learning Based Predictive Model of 5-Year Survival in Multiple Myeloma Autologous Transplant Patients

inferredcites →https://doi.org/10.1182/blood-2019-130483openalex-snapshot

Knowledge Transfer between Artificial Neural Networks for Different Multicolor Flow Cytometry Protocols Improves Classification Performance for Rare B-Cell Neoplasm Subtypes

inferredcites →https://doi.org/10.1182/blood.2020005488openalex-snapshot

Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes

inferredcites →https://doi.org/10.1182/blood.v120.21.2348.2348openalex-snapshot

Image-Based RNA Interference Screening Identifies Microenvironmental Signals Supporting Primary Acute Lymphoblastic Leukemia Cell Survival.

inferredcites →https://doi.org/10.1182/blood.v122.21.409.409openalex-snapshot

Prediction Of Allogeneic Hematopoietic Stem Cell Transplantation (allo-HSCT) Related Mortality in Acute Leukemia: Generation Of a Machine Learning-Based Model Using The Data Set of The Acute Leukemia Working Party (ALWP) Of The EBMT

inferredcites →https://doi.org/10.1182/blood.v126.23.3912.3912openalex-snapshot

Predicting Time to Treatment in Follicular Lymphoma Using Population-Based Data

inferredcites →https://doi.org/10.1182/bloodadvances.2019000934openalex-snapshot

Using a machine learning algorithm to predict acute graft-versus-host disease following allogeneic transplantation

inferredcites →https://doi.org/10.1182/bloodadvances.2020002230openalex-snapshot

Artificial intelligence–based morphological fingerprinting of megakaryocytes: a new tool for assessing disease in MPN patients

inferredcites →https://doi.org/10.1182/bloodadvances.2020002997openalex-snapshot

Application of machine learning in the management of acute myeloid leukemia: current practice and future prospects

inferredcites →https://doi.org/10.1200/cci.17.00011openalex-snapshot

Toward Electronic Surveillance of Invasive Mold Diseases in Hematology-Oncology Patients: An Expert System Combining Natural Language Processing of Chest Computed Tomography Reports, Microbiology, and Antifungal Drug Data

inferredcites →https://doi.org/10.1200/cci.18.00137openalex-snapshot

Improved Interpretability of Machine Learning Model Using Unsupervised Clustering: Predicting Time to First Treatment in Chronic Lymphocytic Leukemia

inferredcites →https://doi.org/10.1309/ajcpy8e2lyhcgufpopenalex-snapshot

Computer-Aided Detection of Rare Tumor Populations in Flow Cytometry

inferredcites →https://doi.org/10.1364/boe.405557openalex-snapshot

Deep learning-based light scattering microfluidic cytometry for label-free acute lymphocytic leukemia classification

inferredcites →https://doi.org/10.1371/journal.pcbi.1007332openalex-snapshot

Dark-matter matters: Discriminating subtle blood cancers using the darkest DNA

inferredcites →https://doi.org/10.1371/journal.pone.0212127openalex-snapshot

Leukemia multiclass assessment and classification from Microarray and RNA-seq technologies integration at gene expression level

inferredcites →https://doi.org/10.1371/journal.pone.0226115openalex-snapshot

A novel one-class classification approach to accurately predict disease-gene association in acute myeloid leukemia cancer

inferredcites →https://doi.org/10.14569/ijacsa.2020.0110646openalex-snapshot

Application of Homomorphic Encryption on Neural Network in Prediction of Acute Lymphoid Leukemia

inferredcites →https://doi.org/10.1515/jib-2019-0069openalex-snapshot

Metalearning approach for leukemia informative genes prioritization

inferredcites →https://doi.org/10.18502/ijpho.v11i2.5838openalex-snapshot

Gene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method

inferredcites →https://doi.org/10.25122/jml-2019-0090openalex-snapshot

Deep Learning for Acute Myeloid Leukemia Diagnosis

inferredcites →https://doi.org/10.2991/chi.k.201130.001openalex-snapshot

Machine Learning Applications in the Diagnosis of Benign and Malignant Hematological Diseases

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Prediction of Cranial Radiotherapy Treatment in Pediatric Acute Lymphoblastic Leukemia Patients Using Machine Learning: A Case Study at MAHAK Hospital

inferredcites →https://doi.org/10.3389/fbioe.2020.01005openalex-snapshot

Automated Detection of Acute Lymphoblastic Leukemia From Microscopic Images Based on Human Visual Perception

inferredcites →https://doi.org/10.3389/fonc.2019.00079openalex-snapshot

Time to Treatment Prediction in Chronic Lymphocytic Leukemia Based on New Transcriptional Patterns

inferredcites →https://doi.org/10.3390/ai1030023openalex-snapshot

A Single Gene Expression Set Derived from Artificial Intelligence Predicted the Prognosis of Several Lymphoma Subtypes; and High Immunohistochemical Expression of TNFAIP8 Associated with Poor Prognosis in Diffuse Large B-Cell Lymphoma

inferredcites →https://doi.org/10.3390/bioengineering7040120openalex-snapshot

Detection and Classification of Immature Leukocytes for Diagnosis of Acute Myeloid Leukemia Using Random Forest Algorithm

inferredcites →https://doi.org/10.3390/cancers12040797openalex-snapshot

Artificial Intelligence and Digital Microscopy Applications in Diagnostic Hematopathology

inferredcites →https://doi.org/10.3390/cancers12061684openalex-snapshot

A Clinically Applicable Approach to the Classification of B-Cell Non-Hodgkin Lymphomas with Flow Cytometry and Machine Learning

inferredcites →https://doi.org/10.3390/cancers13010017openalex-snapshot

High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia

inferredcites →https://doi.org/10.3390/diagnostics11101759openalex-snapshot

Radiomics and Artificial Intelligence for Outcome Prediction in Multiple Myeloma Patients Undergoing Autologous Transplantation: A Feasibility Study with CT Data

inferredcites →https://doi.org/10.3390/diagnostics9030104openalex-snapshot

Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network

inferredcites →https://doi.org/10.3390/electronics9010188openalex-snapshot

Leukemia Image Segmentation Using a Hybrid Histogram-Based Soft Covering Rough K-Means Clustering Algorithm

inferredcites →https://doi.org/10.3892/mmr.2016.5260openalex-snapshot

Feature genes predicting the FLT3/ITD mutation in acute myeloid leukemia

inferredcites →https://doi.org/10.4081/hr.2020.8957openalex-snapshot

Advancing Leukemia Diagnostics: Role of Next Generation Sequencing (NGS) in Acute Myeloid Leukemia

inferredcites →https://doi.org/10.4274/tjh.2018.0123openalex-snapshot

Artificial Intelligence approaches in hematopoietic cell transplant: A review of the current status and future directions

inferredcites →https://doi.org/10.5220/0006595804010408openalex-snapshot

Application of Machine Learning for Automatic MRD Assessment in Paediatric Acute Myeloid Leukaemia

inferredcites →https://doi.org/10.5755/j01.eie.25.5.24358openalex-snapshot

Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

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18 F-FDG PET/CT Radiomic Analysis with Machine Learning for Identifying Bone Marrow Involvement in the Patients with Suspected Relapsed Acute Leukemia

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inferred← citeshttps://doi.org/10.1007/s00277-025-06693-4openalex-snapshot

Current scenario of allogeneic hematopoietic stem cell transplantation in adult patients with relapsed/refractory acute myeloid leukemia: a narrative review

inferred← citeshttps://doi.org/10.1007/s00277-026-06996-0openalex-snapshot

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

inferred← citeshttps://doi.org/10.1007/s10791-026-10307-6openalex-snapshot

Dual-fusion multi-representation deep learning framework for interpretable blood cell classification and malignancy detection

inferred← citeshttps://doi.org/10.1007/s11517-023-02955-3openalex-snapshot

CDC-NET: a cell detection and confirmation network of bone marrow aspirate images for the aided diagnosis of AML

inferred← citeshttps://doi.org/10.1007/s12288-025-02244-zopenalex-snapshot

Artificial Intelligence in Hematology

inferred← citeshttps://doi.org/10.1007/s40745-026-00685-5openalex-snapshot

AML Diagnosis via Transfer Learning: A Modified ResNet50 Approach to Leukocyte Classification

inferred← citeshttps://doi.org/10.1007/s44163-026-00956-3openalex-snapshot

Clinical readiness and limitations of artificial intelligence in hematologic diagnostics: a critical analytical review

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inferred← citeshttps://doi.org/10.1016/j.blre.2023.101144openalex-snapshot

From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film

inferred← citeshttps://doi.org/10.1016/j.bspc.2025.109416openalex-snapshot

MDS-AN: An attention-based explainable model for detecting myelodysplastic syndrome using hematological variables

inferred← citeshttps://doi.org/10.1016/j.cca.2024.119724openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.cll.2025.07.011openalex-snapshot

Machine Learning and Artificial Intelligence-Based Clinical Decision Support for Modern Hematology

inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110178openalex-snapshot

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inferred← citeshttps://doi.org/10.1016/j.ejon.2023.102272openalex-snapshot

Cancer nursing research priorities: A rapid review

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Rapid multiplex detection of bloodstream pathogens using microsphere suspension arrays functionalized with RNA-cleaving fluorogenic DNAzymes

inferred← citeshttps://doi.org/10.1016/j.patrec.2026.06.002openalex-snapshot

Dyna-HemoNet: A lightweight and adaptive framework for accurate blood cell detection

inferred← citeshttps://doi.org/10.1016/j.phrs.2024.107158openalex-snapshot

The new era of immunological treatment, last updated and future consideration of CAR T cell-based drugs

inferred← citeshttps://doi.org/10.1016/j.procs.2026.04.108openalex-snapshot

Decision Support Systems using AI and OR Models for Cancer Prediction: A Systematic Mapping Study

inferred← citeshttps://doi.org/10.1016/j.retram.2026.103602openalex-snapshot

Non-invasive differentiation between aplastic anemia and myelodysplastic syndromes based on an 8-feature lightGBM model: A multicenter external validation study

inferred← citeshttps://doi.org/10.1016/j.rvsc.2025.105650openalex-snapshot

Machine learning-based detection and quantification of red blood cells in Cholistani cattle: A pilot study

inferred← citeshttps://doi.org/10.1038/s41598-025-20605-6openalex-snapshot

Machine learning improves detection of alpha thalassemia carriers compared to clinical features

inferred← citeshttps://doi.org/10.1038/s41598-025-29672-1openalex-snapshot

A personalized communication efficient federated learning framework with low rank adaptation for intelligent leukemia diagnosis

inferred← citeshttps://doi.org/10.1038/s41598-025-34715-8openalex-snapshot

Explainable federated transformer framework for joint leukemia classification and stage prediction

inferred← citeshttps://doi.org/10.1063/5.0244573openalex-snapshot

Blood less hematology cell counter and survival prediction using artificial intelligence

inferred← citeshttps://doi.org/10.1063/5.0327677openalex-snapshot

AI in hematology: A systematic review of techniques for rare blood disorder detection and treatment

inferred← citeshttps://doi.org/10.1080/10408363.2026.2665294openalex-snapshot

Single cell mechanodeformation signatures as early biomarkers for hematological malignancies

inferred← citeshttps://doi.org/10.1080/1744666x.2024.2373915openalex-snapshot

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inferred← citeshttps://doi.org/10.1097/md.0000000000041766openalex-snapshot

Big data analytics and machine learning in hematology: Transformative insights, applications and challenges

inferred← citeshttps://doi.org/10.1097/ms9.0000000000003227openalex-snapshot

Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence

inferred← citeshttps://doi.org/10.1097/ms9.0000000000004325openalex-snapshot

Innovative hematological strategies to combat maternal and child mortality in Africa: focus on anemia and sickle cell disease

inferred← citeshttps://doi.org/10.1109/becithcon64160.2024.10962577openalex-snapshot

VGG19 and Inception V3 Performance Evaluation for Early Leukemia Detection from Blood Smears

inferred← citeshttps://doi.org/10.1109/ccict62777.2024.00064openalex-snapshot

Role of artificial intelligence in the early detection of thrombocytopenia: An approach to improve health of pregnant women

inferred← citeshttps://doi.org/10.1109/icaaic56838.2023.10140293openalex-snapshot

White Blood Cells Recognition and Classification using Convolutional Neural Network

inferred← citeshttps://doi.org/10.1109/icaect68478.2026.11426128openalex-snapshot

Explainable Hybrid Architectures for Hematologic Malignancy Detection in Bone Marrow Smears

inferred← citeshttps://doi.org/10.1109/icscai61790.2024.10867114openalex-snapshot

Blood Cancer Detection Using Machine Learning: A Review

inferred← citeshttps://doi.org/10.1111/bjh.19742openalex-snapshot

The power and perils of large language models in haematology

inferred← citeshttps://doi.org/10.1117/12.3017031openalex-snapshot

Multithreading approach for white blood cell segmentation implementation

inferred← citeshttps://doi.org/10.1155/ijcp/3282116openalex-snapshot

Artificial Intelligence and Digital Pathology: Toward Next‐Generation Diagnostic Hematology

inferred← citeshttps://doi.org/10.1177/10732748241275026openalex-snapshot

Diagnosis Challenges in Adult Leukemia: Insights From a Single-Center Retrospective Study in Qatar (2016-2021)

inferred← citeshttps://doi.org/10.1177/11795972261456588openalex-snapshot

AI-Based Computational Model Integrating Routinely Acquired Blood Parameters for Triage and Early Detection of Acute Myeloid Leukemia

inferred← citeshttps://doi.org/10.1177/20552076261466149openalex-snapshot

NeurALLNet: An attention-based spiking neural network for energy-efficient multi-class classification of acute lymphoblastic leukemia

inferred← citeshttps://doi.org/10.1200/cci-25-00296openalex-snapshot

Study of the Perceptions and Concerns of a Single Health System Hematology and Oncology Workforce About Artificial Intelligence in Clinical Practice and Medical Education

inferred← citeshttps://doi.org/10.1515/cclm-2025-1450openalex-snapshot

Artificial intelligence and machine learning in thrombosis and hemostasis: a scoping review of clinical and laboratory applications, challenges, and future directions

inferred← citeshttps://doi.org/10.18705/2311-4495-2026-13-4-388-398openalex-snapshot

Detection of blast cells on the Mindray MC-80 automatic blood cell analyzer

inferred← citeshttps://doi.org/10.21203/rs.3.rs-2913988/v1openalex-snapshot

Manually-established abnormal karyotype dataset based on normal chromosomes effectively train artificial intelligence model for better cytogenetic abnormalities prediction

inferred← citeshttps://doi.org/10.2174/0118756921438543251212114619openalex-snapshot

AI-Augmented Cytogenetics in Hematologic Malignancies: A Diagnostic Paradigm Shift

inferred← citeshttps://doi.org/10.2196/53892openalex-snapshot

Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study

inferred← citeshttps://doi.org/10.31435/ijitss.2(50).2026.5461openalex-snapshot

CLINICAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN MODERN HEMATOLOGY: DIAGNOSTIC AND PROGNOSTIC PERSPECTIVES

inferred← citeshttps://doi.org/10.3233/shti230479openalex-snapshot

An Artificial Intelligence-Based Diagnostic System for Acute Lymphoblastic Leukemia Detection

inferred← citeshttps://doi.org/10.3233/shti230483openalex-snapshot

Machine Learning for Diagnosis and Screening of Chronic Lymphocytic Leukemia Using Routine Complete Blood Count (CBC) Results

inferred← citeshttps://doi.org/10.3389/fcell.2025.1674393openalex-snapshot

CausalFormer-HMC: a hybrid memory-driven transformer with causal reasoning and counterfactual explainability for leukemia diagnosis

inferred← citeshttps://doi.org/10.3389/fonc.2025.1475893openalex-snapshot

Exploring the role of artificial intelligence in chemotherapy development, cancer diagnosis, and treatment: present achievements and future outlook

inferred← citeshttps://doi.org/10.3389/frai.2026.1763872openalex-snapshot

Hybrid neutrosophic enhanced MobileNetV2 model for leukemia blood cell classification

inferred← citeshttps://doi.org/10.3390/bioengineering12060655openalex-snapshot

AI-Powered Spectral Imaging for Virtual Pathology Staining

inferred← citeshttps://doi.org/10.3390/biomedicines13040835openalex-snapshot

Using Artificial Intelligence to Enhance Myelodysplastic Syndrome Diagnosis, Prognosis, and Treatment

inferred← citeshttps://doi.org/10.3390/cancers16010065openalex-snapshot

Integrating AI and ML in Myelodysplastic Syndrome Diagnosis: State-of-the-Art and Future Prospects

inferred← citeshttps://doi.org/10.3390/cancers18101618openalex-snapshot

Leveraging Ensemble Machine Learning Models for the Detection of Primary Myelofibrosis in Electronic Health Records

inferred← citeshttps://doi.org/10.3390/cancers18162598openalex-snapshot

From Prediction to Intervention: Artificial Intelligence for Adaptive Response and Toxicity Modeling in Cellular Therapies for Hematologic Malignancies

inferred← citeshttps://doi.org/10.3390/diagnostics15162040openalex-snapshot

Neuro-Bridge-X: A Neuro-Symbolic Vision Transformer with Meta-XAI for Interpretable Leukemia Diagnosis from Peripheral Blood Smears

inferred← citeshttps://doi.org/10.3390/hematolrep17030024openalex-snapshot

Artificial Intelligence (AI) and Drug-Induced and Idiosyncratic Cytopenia: The Role of AI in Prevention, Prediction, and Patient Participation

inferred← citeshttps://doi.org/10.3390/ijerph20010526openalex-snapshot

Prediction of Multiple Clinical Complications in Cancer Patients to Ensure Hospital Preparedness and Improved Cancer Care

inferred← citeshttps://doi.org/10.3390/ijms26136100openalex-snapshot

Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization

inferred← citeshttps://doi.org/10.3390/molecules29174280openalex-snapshot

Promising Therapeutic Strategies for Hematologic Malignancies: Innovations and Potential

inferred← citeshttps://doi.org/10.34248/bsengineering.1917276openalex-snapshot

Comparative Analysis of Machine and Deep Learning Methods for Multi-Label Classification of Hematological Conditions Using Complete Blood Count Data

inferred← citeshttps://doi.org/10.4018/979-8-3373-9715-3.ch010openalex-snapshot

AI-Enhanced Precision Medicine for Leukemia

inferred← citeshttps://doi.org/10.4103/kkujhs.kkujhs_42_25openalex-snapshot

Artificial Intelligence in Hematology: Diagnostic Accuracy, Implementation Challenges, and Future Directions: A Systematic Review

inferred← citeshttps://doi.org/10.4103/sjopt.sjopt_153_23openalex-snapshot

Applications of artificial intelligence-assisted retinal imaging in systemic diseases: A literature review

inferred← citeshttps://doi.org/10.47820/recima21.v7i9.8920openalex-snapshot

INTELIGÊNCIA ARTIFICIAL EM HEMATOLOGIA LABORATORIAL: IMPACTO NO DIAGNÓSTICO

inferred← citeshttps://doi.org/10.51643/22562915.767openalex-snapshot

La transformación digital del laboratorio hematológico: análisis de sistemas basados en inteligencia artificial

inferred← citeshttps://doi.org/10.61186/ijbc.15.3.68openalex-snapshot

The application of artificial intelligence (AI) in the diagnosis of platelet disorders

inferred← citeshttps://doi.org/10.61882/ijbc.17.3.46openalex-snapshot

The Role of Artificial Intelligence in Shaping the Future of Hematological Diagnosis and Treatment

inferred← citeshttps://doi.org/10.7759/cureus.49462openalex-snapshot

Surveying Hematologists’ Perceptions and Readiness to Embrace Artificial Intelligence in Diagnosis and Treatment Decision-Making

inferred← citeshttps://doi.org/10.7759/cureus.73274openalex-snapshot

Transformative Role of Artificial Intelligence in Reporting Haematology Cases: A Case Report

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