Toward integrated multimodal artificial intelligence in bone marrow examination: current state and open challenges.

Abstract

Bone marrow (BM) examinations are intrinsically multimodal: hematopathologists integrate peripheral blood, aspirate cytomorphology, trephine histology, flow cytometry, cytogenetics, and molecular genetics data into a single interpretation. Because WHO-HAEM5 and ICC 2022 define entities through integrated morphological, immunophenotypic, cytogenetic, and molecular criteria, multimodal diagnostic reasoning embedded in a procedural workflow, not isolated image classification, represents the appropriate target for the use of artificial intelligence (AI). We review the existing research on integrated multimodal AI systems for BM examination by classifying these systems into three categories: mature single-modality tools; systems that perform cross-modal prediction, in which one modality is estimated from another; and genuine multimodal fusion systems, in which several modalities are jointly ingested by a single model. Evidence is stronger at the single-modality level, where deep learning approaches expert performance in aspirate cytology, trephine histology, and flow cytometry assessments, and the first Food and Drug Administration (FDA) De Novo-authorized all-digital bone marrow aspirate (BMA) application has defined a new device category. On the other hand, while cross-modal prediction is rapidly expanding, genuine multimodal fused systems that jointly ingest matched morphology, flow, molecular, and clinical data to yield unified guideline-aligned recommendations remain unrealized. Direct head-to-head evidence is sparse and endpoint-dependent; nevertheless, the lack of demonstrated superiority over the strongest single-modality comparator does not automatically imply the absence of benefits. The dominant obstacles are fusion of heterogeneous modalities with missing data, absence of large paired multi-institutional datasets and harmonized nomenclature, class imbalances, batch-effect-driven generalization failures, immature explainability, uncertainty quantification, and regulatory pathways. Nevertheless, integrated multimodal AI systems for BM examinations have entered a promising early translational phase, and the field should aim to pursue integration using paired datasets, explicit fusion architectures, calibrated abstention-aware models, and staged prospective validation.