Smart Hematology

Smart Hematology: Traditional and AI-Powered Diagnosis and Management addresses a pressing challenge in contemporary hematological practice: how to thoughtfully integrate emerging artificial intelligence technologies with established diagnostic methods. As machine learning tools increasingly enter clinical laboratories and hematology departments, there exists a significant gap in literature that provides both comprehensive theoretical grounding and practical implementation guidance for these approaches alongside traditional methodologies. This edited volume brings together international experts to present what they believe is the first systematic integration of conventional and AI-assisted approaches across the spectrum of hematological disorders. Rather than treating ML as a separate or competing paradigm, each section is structured to demonstrate how these technologies can complement and enhance established diagnostic techniques, from microscopic examination of blood films and bone marrow specimens to flow cytometry, molecular diagnostics, and hemostasis testing. What distinguishes this work is its emphasis on practical translation. Each chapter not only explains algorithmic approaches but critically examines their clinical validity, limitations, and real-world implementation challenges. Contributors draw on published research and clinical experience to present realistic applications, such as automated leukemia subtyping, predictive models for treatment response, and integration of multi-modal diagnostic data—while maintaining appropriate clinical skepticism about oversimplified technological solutions. This book prepares the current and next generation of hematology professionals to work effectively in an evolving diagnostic landscape where human expertise and computational tools work in concert to improve patient care. It is intended primarily for practicing hematologists, clinical pathologists, and laboratory medicine specialists who recognize the growing role of computational tools in their field but may lack formal training in their application. It also serves hematology fellows, PhD students, and medical laboratory scientists seeking to understand both the clinical context and technical underpinnings of AI-assisted diagnostics.