ReviewCurrent hematologic malignancy reports2020
Artificial Intelligence in Hematology: Current Challenges and Opportunities.
Review in Current hematologic malignancy reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Multi-Omics Characterization of Blood Cancers: A Systematic Review.Cells · 2025Pooled it
- Machine learning models in predictive factors for megaloblastic character of macrocytic anemia.Leukemia research reports · 2026Article
- Chondrogenic Differentiation of Stem Cells for Cartilage Regeneration: Advances and Future Perspectives.Tissue engineering and regenerative medicine · 2026Review
- How Could Artificial Intelligence Change the Doctor-Patient Relationship? A Medical Ethics Perspective.Healthcare (Basel, Switzerland) · 2025Review
- Artificial Intelligence in the Management of Hereditary and Acquired Hemophilia: From Genomics to Treatment Optimization.International journal of molecular sciences · 2025Review
- Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.Annals of medicine and surgery (2012) · 2025Review
- Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms.Visual computing for industry, biomedicine, and art · 2024Article
- Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Artificial intelligence to assist specialists in the detection of haematological diseases.Heliyon · 2023Article
- AI for Anglophone Africa: Unlocking its adoption for responsible solutions in academia-private sector.Frontiers in artificial intelligence · 2023Article
- Cell projection plots: A novel visualization of bone marrow aspirate cytology.Journal of pathology informatics · 2023Article
- A multiple instance learning approach for detecting COVID-19 in peripheral blood smears.PLOS digital health · 2022Article
- Clinical Applications of Artificial Intelligence-An Updated Overview.Journal of clinical medicine · 2022Review
- Article
- Automated bone marrow cytology using deep learning to generate a histogram of cell types.Communications medicine · 2022Article
- Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms.Patterns (New York, N.Y.) · 2021Article
- Recent trends in stem cell-based therapies and applications of artificial intelligence in regenerative medicine.World journal of stem cells · 2021Review
- Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.Biomolecules · 2021Article
- High-Dimensional Analysis of Single-Cell Flow Cytometry Data Predicts Relapse in Childhood Acute Lymphoblastic Leukaemia.Cancers · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purpose of reviewArtificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators. RECENT
findingsAI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine. AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.
Indexed as
Identifiers
32239350What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.