Artificial Intelligence and Machine Learning in Hematology: Transforming Diagnosis, Prognosis, and Personalized Treatment

Hematology stands at the crossroads of data-intensive science and precision medicine. Over decades, hematologists have relied on complex datasets from blood counts and cytogenetics to multi-omics and flow cytometry to guide diagnosis and treatment. Artificial intelligence (AI) and machine learning (ML) now offer unprecedented tools to augment this interpretive process. Recent advances have revealed their potential in morphological analysis, risk stratification, and outcome prediction across conditions such as leukemias, lymphomas, and myelodysplastic syndromes. Despite remarkable progress, critical gaps persist regarding model interpretability, clinical integration, and ethical deployment. The field now faces pressing questions about how computational methods can enrich human expertise without compromising patient safety or the humanistic ethos of medicine.This Research Topic aims to explore the transformative role of AI and ML in contemporary hematology. It seeks to consolidate emerging evidence on applications spanning the full diagnostic and therapeutic continuum from automated image analysis to predictive modeling and multi-omics integration. Specific objectives include evaluating methodologies for accuracy, reproducibility, and fairness; fostering approaches that address bias and explainability; and identifying pathways for clinical validation and regulatory compliance. Ultimately, the goal is to define how computational intelligence can be ethically and effectively embedded into hematologic research and practice to enhance patient outcomes and healthcare equity.To gather further insights into the intersection of computational innovation and clinical hematology, we welcome articles addressing, but not limited to, the following themes:• Deep learning techniques for morphological and cytometric data interpretation• AI-based integration of genomic, transcriptomic, and proteomic data• Predictive and prognostic modeling in malignant and benign hematologic diseases• Natural language processing and electronic health record mining for hematologic insights• Federated learning and privacy-preserving algorithms for multi-center data sharing• Development of AI-driven decision-support tools for clinical workflows• Ethical, regulatory, and interpretability challenges in AI deployment• Digital twins, robotic automation, and next-generation laboratory infrastructure• Methodological innovation under data scarcity in pediatric or rare hematologic conditionsWe welcome various article types, including original research and review articles, as well as replication studies and negative findings. Submissions encompassing pediatric hematology, transfusion medicine, and hematopoietic stem cell transplantation are particularly encouraged.Topic Editor Vasiliki Danilatou is the founder and shareholder of SME Eunomia Ltd. All other Topic Editors declare no conflicts of interest. Topic editors: Eleftheria Hatzimichael, Savvas A Chatzichristofis, Vasiliki Danilatou Source: https://www.frontiersin.org/research-topics/80124/artificial-intelligence-and-machine-learning-in-hematology-transforming-diagnosis-prognosis-and-personalized-treatment