ReviewFrontiers in medicine2024
Optimization of diagnosis and treatment of hematological diseases via artificial intelligence.
Review in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 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
- Artificial Intelligence for Personalized Management of Acute Myeloid Leukemia.Journal of personalized medicine · 2026Review
- Machine learning-guided risk stratification in elderly AML based on genomic, immunophenotypic and therapeutic profiles.BMC geriatrics · 2026Article
- Explainable federated transformer framework for joint leukemia classification and stage prediction.Scientific reports · 2026Article
- Omics and artificial intelligence integration for stratifying blast crisis CML using COSMIC signatures and pan-cancer precision drug repurposing.World journal of clinical oncology · 2025Article
- Early detection and better outcomes: molecular approaches in pediatric hematologic malignancies - a review.Annals of medicine and surgery (2012) · 2025Review
- The role of advanced diagnostics on precision medicine in hemato oncology.Discover oncology · 2025Review
- Artificial Intelligence (AI) and Drug-Induced and Idiosyncratic Cytopenia: The Role of AI in Prevention, Prediction, and Patient Participation.Hematology reports · 2025Review
- Interpretable machine learning models for beta thalassemia prediction: an explainable AI approach for smart healthcare 5.0.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Optimizing the diagnosis and treatment of hematological diseases is a challenging yet crucial research area. Effective treatment plans typically require the comprehensive integration of cell morphology, immunology, cytogenetics, and molecular biology. These plans also consider patient-specific factors such as disease stage, age, and genetic mutation status. With the advancement of artificial intelligence (AI), more "AI + medical" application models are emerging. In clinical practice, many AI-assisted systems have been successfully applied to the diagnosis and treatment of hematological diseases, enhancing precision and efficiency and offering valuable solutions for clinical practice. Objective: This study summarizes the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a focus on their application in morphology, immunology, cytogenetics, and molecular biology diagnosis, as well as prognosis prediction and treatment. Methods: Using PubMed, Web of Science, and other network search engines, we conducted a literature search on studies from the past 5 years using the main keywords "artificial intelligence" and "hematological diseases." We classified the clinical applications of AI systems according to the diagnosis and treatment. We outline and summarize the current advancements in AI for optimizing the diagnosis and treatment of hematological diseases, as well as the difficulties and challenges in promoting the standardization of clinical diagnosis and treatment in this field. Results: AI can significantly shorten turnaround times, reduce diagnostic costs, and accurately predict disease outcomes through applications in image-recognition technology, genomic data analysis, data mining, pattern recognition, and personalized medicine. However, several challenges remain, including the lack of AI product standards, standardized data, medical-industrial collaboration, and the complexity and non-interpretability of AI systems. In addition, regulatory gaps can lead to data privacy issues. Therefore, more research and improvements are needed to fully leverage the potential of AI to promote standardization of the clinical diagnosis and treatment of hematological diseases. Conclusion: Our results serve as a reference point for the clinical diagnosis and treatment of hematological diseases and the development of AI-assisted clinical diagnosis and treatment systems. We offer suggestions for further development of AI in hematology and standardization of clinical diagnosis and treatment.
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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 Socratic graph.