Abstract
Bone marrow biopsy remains central to the diagnosis, classification, and monitoring of hematologic disorders, providing essential morphologic and immunophenotypic information. However, conventional assessment is limited by interobserver variability and an inability to fully capture the molecular and spatial complexity of marrow pathology. Recent advances in multi-omics technologies, including genomic, epigenomic, transcriptomic, proteomic, lipidomic, metabolomic, and microbiomic, together with artificial intelligence (AI), are reshaping bone marrow evaluation. These approaches generate high-dimensional datasets that reveal disease-specific molecular signatures, refine diagnostic classification, improve risk stratification, and inform therapeutic decision-making. In parallel, AI-driven image analysis enhances objectivity, reproducibility, and integration of morphologic and molecular data across both biopsy histology and aspirate cytology. Despite their transformative potential, clinical translation remains challenged by issues of standardization, cost, data integration, and regulatory validation. This mini-review summarizes emerging applications of omics technologies and AI in bone marrow biopsy diagnostics, highlights current limitations, and discusses future directions toward integrated, data-driven precision hematology.