Machine learning for AML-MRD - Ep 3.3

Machine learning for AML-MRD - Ep 3.3

From HemaSphere Podcast by HemaSphere Journal

July 2, 2025 · 29 min · Season 3 · Episode 3

About this episode

The episode discusses the role of machine learning in assessing measurable residual disease in acute myeloid leukemia.

In this episode of the HemaSphere podcast, host Charles de Bock and guest Tim Mocking, PhD, discuss the role of machine learning in the assessment of measurable residual disease (MRD) in acute myeloid leukemia (AML). They explore the challenges of MRD detection, the potential of machine learning to improve accuracy and efficiency, and the importance of understanding the human element in computational methods. The conversation also touches on the adoption of AI in hematology, the future of flow cytometry, and the implications of new technologies for patient care. Episode 3.3 - Machine learning for AML-MRD is based on the recently published Review Article " Applications of machine learning for immunophenotypic measurable residual disease assessment in acute myeloid leukemia ”, is on our website , all major podcast platforms, and YouTube. Listen and enjoy casual, insightful discussions about #hematology research. You can find the referenced article, in full and open access, here on the HemaSphere website .

People in this episode

Host: Charles de Bock

Guest: Tim Mocking, PhD

Topics covered

  • machine learning
  • acute myeloid leukemia
  • measurable residual disease
  • AI in hematology
  • flow cytometry
  • patient care

Keywords

  • machine learning
  • acute myeloid leukemia
  • measurable residual disease
  • AI
  • flow cytometry
  • hematology
  • patient care

Mentioned in this episode

Organizations: HemaSphere Journal

Books & works: Applications of machine learning for immunophenotypic measurable residual disease assessment in acute myeloid leukemia

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