Advancements in Personalized Medicine for Leukemia: Integrating Genetic, Transcriptomic, and Artificial Intelligence Insights

Leukemia, a heterogeneous group of hematologic malignancies, has significantly benefited from advancements in diagnosis, classification, and treatment, particularly through precision medicine. This review examines the role of genetic and molecular profiling, transcriptomics, and artificial intelligence (AI) in advancing precision medicine for leukemia. Key genetic alterations and molecular abnormalities driving leukemogenesis, along with their implications for targeted therapies, are addressed. Insights from RNA sequencing and single-cell RNA sequencing (scRNA-seq) have facilitated the identification of novel therapeutic targets and enhanced risk stratification. Furthermore, AI-driven models, including machine learning (ML) and deep learning (DL) algorithms, have improved leukemia diagnosis, prognosis, and treatment optimization. Despite these advancements, challenges such as clonal evolution, genetic heterogeneity, and treatment resistance remain. The integration of multi-omics data and emerging technologies holds promise for refining personalized therapeutic strategies, ultimately improving patient survival and quality of life.