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AuthorEl Alaoui, Yousra
AuthorElomri, Adel
AuthorQaraqe, Marwa
AuthorPadmanabhan, Regina
AuthorYasin Taha, Ruba
AuthorEl Omri, Halima
AuthorEL Omri, Abdelfatteh
AuthorAboumarzouk, Omar
Available date2024-07-22T09:19:58Z
Publication Date2022
Publication NameJournal of Medical Internet Research
ResourceScopus
Identifierhttp://dx.doi.org/10.2196/36490
ISSN14388871
URIhttp://hdl.handle.net/10576/56906
AbstractBackground: Machine 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. Objective: This 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. Methods: We searched a set of recognized databases (Scopus, Springer, and Web of Science) using a selected number. 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. Results: Using the aforementioned inclusion criteria, the search resulted in 567 papers, of which 144 were selected for review. Conclusions: The 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.
SponsorThis article was made possible by the National Priorities Research Program-Standard (NPRP-S) Twelfth (12th) Cycle grant (NPRP12S-0219-190108) from the Qatar National Research Fund (a member of Qatar Foundation). The findings herein reflect the work, and are solely the responsibility, of the author[s].
Languageen
PublisherJMIR Publications
Subjectartificial intelligence
cancer
deep learning
hematology
machine learning
malignancy
management
oncology
prediction
TitleA Review of Artificial Intelligence Applications in Hematology Management: Current Practices and Future Prospects
TypeArticle Review
Issue Number7
Volume Number24
dc.accessType Abstract Only


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