Unraveling genotype–phenotype associations and predictive modeling of outcome in acute myeloid leukemia

, based on computationally quantified leukemic cell populations and limited clinical data, both readily available at diagnosis. We used explainable artificial intelligence (AI) to identify the key clinical characteristics and leukemic cell populations important for our ML models when making these predictions. Our findings highlight the importance of developing objective computational pipelines integrating immunophenotypic and genetic information in the risk stratification of AML.