ReviewAnnals of hematology2025
Artificial intelligence in hematology: current trends and application areas.
Review in Annals of hematology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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
5 citing papers in PubMed.
- Putting the I in AML: Artificial Intelligence and Machine Learning in Acute Myeloid Leukemia.Cells · 2026Review
- Review
- Hematology-Oncology Fellows' Use of Artificial Intelligence: A Multicenter Educational Practice and Needs Assessment Survey.Journal of cancer education : the official journal of the American Association for Cancer Education · 2026Article
- An interpretable machine learning model integrating peripheral blood lncRNAs and clinical variables for phase classification in chronic myeloid leukemia.BMC medical informatics and decision making · 2026Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to map the current research trends, application areas, and thematic clusters of artificial intelligence (AI) technologies in hematology. A literature search was conducted in the Web of Science on June 1, 2025, covering the years 1980-2025. A total of 376 original research articles were analyzed using bibliometric techniques, including trend keyword and factor analyses, via the bibliometrix package in R Studio. The USA (n = 111), China (79), the United Kingdom (22), and Germany (21) were the most productive countries, with Harvard University as the leading institution. Keywords revealed research concentration in thrombosis, venous thromboembolism, risk assessment, hematopoietic stem cell transplantation, atrial fibrillation, anticoagulants, morphological analysis, blood management, sepsis, and acute myeloid leukemia. Recent years have shown rising interest in transcriptome, prediction modeling, hematopoietic stem cell transplantation, blood management, thrombosis, venous thromboembolism cancer and COVID-19 related complications. Factor analysis grouped the literature into five clusters: AI and core hematologic diseases, clinical quality improvement, risk prediction using health data, intensive care, and cardiovascular applications. AI demonstrates substantial contributions to diagnostic accuracy, prognostication, and personalized care in hematology. The findings highlight AI's growing potential in enhancing clinical decision-making and improving patient outcomes through data-driven and genomics-based innovations.
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What 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.