Paper Title
Artificial Intelligence Enhanced Haematological Morphology Diagnostics: Innovations And Applications

Abstract
Introduction: Artificial intelligence (AI) is revolutionizing the field of hematology by enhancing diagnostic accuracy and efficiency. The advent of artificial intelligence (AI) has ushered in a transformative era in the diagnosis and management of hematological morphology disorders. This review explores the integration of AI technologies in the diagnostic process, focusing on their application in the analysis of blood smears, bone marrow biopsies, and other hematological assessments. Materials and Methods: A comprehensive literature search was conducted to identify relevant and highly cited studies on the application of AI in diagnostic hematology morphology. Out of 155 reviews, 44 were included as per inclusion and exclusion criteria The search was performed using databases such as PubMed, Scopus, and Google Scholar, focusing on articles published in English between January 2014 and December 2024. Results: AI algorithms, particularly those utilizing machine learning and deep learning techniques, have demonstrated remarkable accuracy in identifying and classifying morphological abnormalities, such as in cases of anemia, leukemia, and various blood dyscrasias. By automating the detection of subtle morphological changes that might be overlooked by human observers, AI enhances diagnostic precision, reduces variability, and expedites the diagnostic workflow.This review highlights the key advancements in AI-driven diagnostic tools, including convolutional neural networks (CNNs) for image analysis, and the role of AI in augmenting traditional hematopathology. Moreover, the paper discusses the challenges faced in the implementation of AI in clinical settings, such as the need for large, high-quality annotated datasets, the risk of algorithmic bias, and the importance of integrating AI tools with existing diagnostic workflows Keywords - Artificial Intelligence, AI, Hematology, Morphology,Machine Learning, Deep Learning