Table 2.

Artificial intelligence (AI) applications in multiple myeloma prognosis and prediction of response to treatment.

Prognosis
Parameters	AI Tools		Ref.	Key Findings
Laboratory parameters	k-adaptive partitioning		[54]	AI-supported modified risk staging for multiple myeloma
Beta2microglobulin	Infinicyt software		[55]	Next-Generation Flow and ML for highly sensitive detection of minimal residual disease
Gene expression profile, ISS stage, first line therapy	Random Forest		[56]	Survival prediction and treatment optimization using ML models based on clinical and gene expression data
mRNA expression-based steamness index	One-class logistic regression		[57]	Analysis of gene expression via one-class logistic regression ML identifies stemness features in MM
Prediction of Response to Treatment
Drugs	Parameters	AI tools	Ref.
Bortezomib, carfilzomib, ixazomib, oprozomib	Gene expression profile	Random Forest	[58]	A gene expression signature distinguishes resistance to proteasome inhibitors
Proteasome inhibitors	Gene complex	Simulated Treatment learning signature	[59]	Gene networks constructed using simulated treatment learning can predict proteasome inhibitor benefit
PAD, VCD	Gene evaluation	Random Forest, Support Vector Machine, Ridge Regression, Binomial Naïve Bayes, Multi-layer perception	[60]	ML applicability for classification of chemotherapy response using 53 MM RNA-sequencing profiles
Five first-line treatments (Bor-Cyc-Dex, Bor-Dex, Bor-Len-Dex, Len-Dex, Non-treatment)	Clinical markers, gene evaluation	Multi Learning Training approach	[61]	ML predicts treatment sensitivity in MM based on molecular and clinical information coupled with drug response

Bor, bortezomib; Cyc, cyclophosphamide; Dex, dexamethasone; Len, lenalidomide.