Artificial intelligence in hematopoietic stem cell research and associated malignancies: From disease modeling to cell manufacturing

Table 4 Artificial intelligence performance vs conventional methods in acute myeloid leukemia and multiple myeloma risk stratification

Disease	Conventional method	AI method	Key performance advantages	Ref.
AML	ELN risk stratification	Multi-omics deep learning	Outperforms ELN-based approaches. Identifies LSC burden (not quantified by ELN). > 90% accuracy in therapy resistance prediction. Integrates clinical, cytogenetic, and molecular data	[53]
AML	Traditional cytogenetic/molecular classification	Supervised machine learning	Superior prediction of complete remission and 2-year survival. External validation confirms generalizability	[17]
MM	Standard risk assessment	Multi-omics integration (neural networks)	Accuracy in therapy resistance prediction. Enhanced drug resistance prediction. Integration of genomic biomarkers and clinical parameters	[31,32,98]
MM	Standard histopathology	Deep learning (MoSaicNet & AwareNet)	Spatial heterogeneity detection beyond cell density. Differentiates MGUS from MM based on spatial architecture	[30]
HSCT GVHD	Cox proportional hazard models	CNN-NLP hybrid	Superior risk stratification. Stratifies aGVHD incidence from 31.8% to 54.8%. Processes detailed HLA information vs binary matching	[41]
HSCT cGVHD	NIH consensus severity criteria	Machine learning phenotyping	Identified 7 distinct phenotypes. 2.24-fold mortality difference between risk groups. Better survival stratification than traditional scores	[42]

AI: Artificial intelligence; AML: Acute myeloid leukemia; ELN: European Leukemia Net; LSC: Leukemic stem cell; MM: Multiple myeloma; MGUS: Monoclonal gammopathy of undetermined significance; HSCT: Hematopoietic stem cell transplantation; GVHD: Graft-vs-host disease; CNN: Convolutional neural network; NLP: Natural language processing; aGVHD: Acute graft-vs-host disease; HLA: Human leukocyte antigen; cGVHD: Chronic graft-vs-host disease; NIH: National Institutes of Health.