Artificial intelligence (AI) has emerged as a revolutionary tool in hematology, capable of analyzing, interpreting, and integrating complex biological, morphological, and genomic data with enhanced precision and speed. This review aims to explore the applications of AI in hematology, with a focus on diagnostic automation, flow cytometry, genomic analysis, predictive modeling, and clinical decision support. A narrative literature review was conducted using PubMed, ScienceDirect, and Google Scholar, covering publications from 2018 to 2024. Keywords included: artificial intelligence, machine learning, deep learning, hematology, cytology, genomics, blood cancers. Articles presenting clinically validated or promising applications were selected. The findings indicate that AI is particularly applied in automated diagnostics and morphological analysis, especially in blood cell classification and the detection of morphological abnormalities. It also plays a pivotal role in flow cytometry and genomics, notably through the interpretation of next-generation sequencing (NGS) data to identify mutations and clinically relevant risk profiles, particularly in acute myeloid leukemia (AML). Furthermore, AI contributes to predictive modeling, patient follow-up, and morphometric quantitative image analysis, facilitating the early detection of hematologic malignancies. In conclusion, AI represents a major technological breakthrough in hematology, complementing human expertise while reducing workload. Future efforts should focus on enhancing algorithm explainability and clinical Regulatory harmonization and close collaboration between clinicians, data scientists, and institutions are essential to fully harness the potential of AI in the diagnosis, prognosis, and treatment of blood disorders.
Bahyat et al. (Tue,) studied this question.