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Artificial Intelligence in Hematology: Current Challenges and Opportunities


Purpose of Review

Artificial intelligence (AI), and in particular its subcategory machine learning, is finding an increasing number of applications in medicine, driven in large part by an abundance of data and powerful, accessible tools that have made AI accessible to a larger circle of investigators.

Recent Findings

AI has been employed in the analysis of hematopathological, radiographic, laboratory, genomic, pharmacological, and chemical data to better inform diagnosis, prognosis, treatment planning, and foundational knowledge related to benign and malignant hematology. As more widespread implementation of clinical AI nears, attention has also turned to the effects this will have on other areas in medicine.


AI offers many promising tools to clinicians broadly, and specifically in the practice of hematology. Ongoing research into its various applications will likely result in an increasing utilization of AI by a broader swath of clinicians.

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Correspondence to Aziz Nazha.

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All reported studies/experiments with human or animal subjects performed by the authors have been previously published and complied with all applicable ethical standards (including the Helsinki declaration and its amendments, institutional/national research committee standards, and international/national/institutional guidelines).

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This article is part of the Topical Collection on Social Media Impact of Hematologic Malignancies

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Radakovich, N., Nagy, M. & Nazha, A. Artificial Intelligence in Hematology: Current Challenges and Opportunities. Curr Hematol Malig Rep 15, 203–210 (2020).

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  • Machine learning
  • Artificial intelligence
  • Hematology
  • Deep learning