Bridging the Diagnostic Gap: The Integration of Artificial Intelligence in Modern Hematology- A Narrative Review

Abstract

Background: Artificial intelligence (AI) has emerged as a transformative technology in hematology, addressing the increasing complexity of hematologic diagnosis and the growing demand for rapid, accurate, and reproducible interpretation of morphological, immunophenotypic, cytogenetic, and molecular data. Machine learning (ML) and deep learning (DL) techniques have demonstrated significant potential to augment conventional diagnostic workflows and support precision medicine.Objective: To provide a comprehensive narrative review of the research progress of various AI-assisted systems applied in the clinical diagnosis and treatment of hematological diseases, with a specific focus on their utility in morphology, immunology, cytogenetics, molecular biology, prognosis prediction, and therapeutic planning, while highlighting existing challenges and future directions for clinical implementation. Key Findings: AI technologies, including image-recognition, genomic data analysis, and pattern recognition, significantly shorten turnaround times, reduce diagnostic costs, and enhance the accuracy of disease outcome predictions. Despite these gains, implementation is constrained by the lack of standardized AI products and datasets, limited medical–industrial collaboration, the "black box" nature of complex AI systems, and regulatory concerns regarding data privacy.Conclusion: Artificial intelligence is poised to become an integral component of diagnostic hematology by improving diagnostic accuracy and workflow efficiency. Although AI is unlikely to replace hematologists, it will increasingly function as a valuable decision-support tool. Continued multicenter validation, regulatory oversight, and interdisciplinary collaboration are essential for safe and effective integration of AI into routine hematology practice.