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ONCOLOGY, NUCLEAR MEDICINE AND TRANSPLANTOLOGY
Review Article
Aug 11, 2026 4 3

From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics

Chidinma Lorretta Gab-Obinna 1 * ,
Daniel Obinna Eke 2,
Jane Ifeyinwa Anatuanya 3,
Blessing Nwakaego Enenmoh. 4
1 Biomedical Science Department, Kingston University London, England, UK2 Department of Nursing, Myrtle E. and Earl E. Walker College of Health Professions, Maryville University of St. Louis, St. Louis, MO, USA3 Haematology and Blood Transfusion Department, University Hospitals Plymouth NHS Trust, UK 4 Blood Sciences Department, NHS Highland, Scotland, UK* Corresponding Author
Oncology, Nuclear Medicine and Transplantology, 2(3), 2026, onmt026, https://doi.org/10.63946/onmt/19128
Publication date: Aug 11, 2026
Full Text (PDF)

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.

KEYWORDS

Artificial Intelligence Hematologic Diagnostics Digital Pathology Precision Hematology Machine Learning

CITATION (Vancouver)

Gab-Obinna CL, Eke DO, Anatuanya JI, Enenmoh. BN. From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics. Oncology, Nuclear Medicine and Transplantology. 2026;2(3):onmt026. https://doi.org/10.63946/onmt/19128
APA
Gab-Obinna, C. L., Eke, D. O., Anatuanya, J. I., & Enenmoh., B. N. (2026). From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics. Oncology, Nuclear Medicine and Transplantology, 2(3), onmt026. https://doi.org/10.63946/onmt/19128
Harvard
Gab-Obinna, C. L., Eke, D. O., Anatuanya, J. I., and Enenmoh., B. N. (2026). From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics. Oncology, Nuclear Medicine and Transplantology, 2(3), onmt026. https://doi.org/10.63946/onmt/19128
AMA
Gab-Obinna CL, Eke DO, Anatuanya JI, Enenmoh. BN. From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics. Oncology, Nuclear Medicine and Transplantology. 2026;2(3), onmt026. https://doi.org/10.63946/onmt/19128
Chicago
Gab-Obinna, Chidinma Lorretta, Daniel Obinna Eke, Jane Ifeyinwa Anatuanya, and Blessing Nwakaego Enenmoh.. "From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics". Oncology, Nuclear Medicine and Transplantology 2026 2 no. 3 (2026): onmt026. https://doi.org/10.63946/onmt/19128
MLA
Gab-Obinna, Chidinma Lorretta et al. "From Microscopy to Precision Hematology: The Evolution of Artificial Intelligence in Hematologic Diagnostics". Oncology, Nuclear Medicine and Transplantology, vol. 2, no. 3, 2026, onmt026. https://doi.org/10.63946/onmt/19128

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