Year: 2024
Type: article
Abstract: There is a broad spectrum of hematological diseases, and their origins can be attributed to a variety of factors, including genetic abnormalities such as leukemia, sickle cell anemia (SCA), and thalassemia, as well as conditions associated with the lack of certain blood components, such as iron deficiency anemia (IDA). Testing and analyzing for hematological disorders is intensive in terms of time, effort, and labor. Additionally, there is a higher chance of human error and variance during the manual examination and analysis of the test samples, depending on the expertise, skills, and experience of the examiner. Recent developments in artificial intelligence (AI), such as Machine Learning (ML) and Deep Learning (DL) algorithms—such as Random Forest (RF), Decision Tree (DT), and Support Vector Machine (SVM)—have demonstrated the considerable contribution they could make to more rapid and accurate disease diagnosis, detection, and classification. An increasing number of hematological diseases are being diagnosed using AI techniques, which combine tabular and image data to eliminate human error, generate more precise results, and decrease the time required for diagnosis. This review discusses several widely utilized AI disease evaluation algorithms and their applicability to hematological disorders. Additionally, we highlight key challenges such as the lack of accessible clinical data, which inhibits the implementation of AI in the field of medicine.
Source: Biomedical Research and Therapy
Institutions: Universiti Sains Malaysia, Mutah University
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: diamond
Funder: Universiti Sains Malaysia
OpenAlex ID: W4406317476