Introduction: Acute myeloid leukemia (AML) is a heterogenous hematologic malignancy that maintains high relapse rates and poor survival despite ongoing treatment advances. There is critically unmet need for consistently providing long-term survival with minimal treatment toxicity for AML patients. Advances in artificial intelligence/machine learning (AI/ML) offer new approaches to addressing clinical challenges in AML. Methods: In this systematic narrative review, 426 publications focusing on the intersection of AML and AI/ML between January 1st 2010 and July 30th 2024 are reviewed. Results: The evolution of AI/ML tools over time is described from a clinically relevant perspective with a distinction between early epochs of AI/ML versus more contemporary algorithms, such as generative adversarial networks (GAN) and transformer-based algorithms. This review highlights the utilization of contemporary AI/ML algorithms via addressing diagnostic challenges, molecular risk stratification problems, and clinical outcome prediction in the context of AML. Conclusion: Overall, AI/ML represents a promising new frontier in approaching clinical problems in AML, though there are still opportunities for utilization, particularly in the setting of allogeneic stem cell transplantation (ASCT).
Sabile et al. (Thu,) studied this question.
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