Keyword: Precision Hematology

ABSTRACT: Hematologic diagnostics has evolved considerably over the past decades, progressing from reliance on manual microscopic examination to increasingly sophisticated digital and artificial intelligence (AI)-enabled diagnostic systems. This transformation has been driven by the growing complexity of hematologic diseases, advances in molecular medicine, and the need for faster, more standardized, and more reproducible diagnostic approaches. AI has emerged as a transformative technology capable of enhancing image interpretation, automating routine laboratory processes, and integrating diverse sources of diagnostic information to support precision medicine. This narrative review critically examines the evolution of hematologic diagnostics from conventional microscopy to AI-assisted practice and explores how AI is reshaping diagnostic workflows across the hematologic diagnostic pathway. Current evidence indicates that AI has substantially improved blood smear analysis, bone marrow evaluation, disease detection and classification, and clinical decision support while reducing observer variability and improving laboratory efficiency. Moreover, the integration of morphology with flow cytometry, cytogenetics, genomics, laboratory findings, and clinical metadata is redefining hematologic diagnosis as a multidimensional, patient-centered process. Despite these advances, important challenges remain, including limited external validation, dataset heterogeneity, algorithm transparency, interoperability, regulatory oversight, and equitable implementation across diverse healthcare settings. The review further identifies key evidence gaps and proposes the Precision Hematology Evolution Framework (PHEF) as a conceptual model illustrating the transition from morphology-based diagnosis to AI-enabled precision hematology. Collectively, the evidence suggests that AI is best viewed as an enabling technology that augments expert clinical interpretation rather than replacing it. Continued multidisciplinary collaboration, rigorous validation, and responsible governance will be essential to fully realize the promise of AI in delivering more accurate, personalized, and equitable hematologic care.