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ASCO 2026 | The evolving role of AI in myeloid malignancies
In this video, Jowan Al-Nusair, MD, Marshall University, Huntington, WV, briefly discusses the evolving role of artificial intelligence (AI) in myeloid malignancies, including acute myeloid leukemia (AML) and myelodysplastic syndromes (MDS). She highlights the potential of machine learning to enhance diagnosis, prognostication, treatment prediction, and research. This interview took place during the 2026 American Society of Clinical Oncology (ASCO) Meeting in Chicago, IL.
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Transcript
I also want to talk about our article that was just published in Blood Reviews. I had the opportunity to work with Dr. Kewan and Dr. Zeidan from Yale University, where we basically looked at clinical applications of machine learning in specifically acute myeloid leukemia and myelodysplastic syndrome. And we can see that they enhance multiple aspects of management in AML and MDS, whether that’s in diagnostics and classification, prognostication, treatment response, and even as research tools...
I also want to talk about our article that was just published in Blood Reviews. I had the opportunity to work with Dr. Kewan and Dr. Zeidan from Yale University, where we basically looked at clinical applications of machine learning in specifically acute myeloid leukemia and myelodysplastic syndrome. And we can see that they enhance multiple aspects of management in AML and MDS, whether that’s in diagnostics and classification, prognostication, treatment response, and even as research tools. So for example, in diagnostics, you know, deep learning methods applied to bone marrow smears, flow cytometry have really outperformed even like conventional methods in terms of sensitivity and specificity. In treatment response, like you can look for responses to hypomethylating agents and venetoclax-based regimens. And in research, machine learning, you know, could help create some synthetic cohorts and digital twins, also facilitating trial design and overcoming different data limitations.
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