AI-assisted multimodal data integration for precision oncology
TL;DR
The landscape of unimodal data modalities used in oncological practice including clinical records, multi-scale imaging, and multi-omics signatures is examined, alongside a critical comparison of the deep-learning architectures and integration frameworks used to combine these data sources into advanced predictive models.
Abstract
The complexity of data pertaining to patients diagnosed with cancer requires a shift from fragmented, unimodal diagnostics towards multimodal artificial intelligence (MAI) in order to achieve true precision oncology. In this literature review we examined the landscape of unimodal data modalities used in oncological practice including clinical records, multi-scale imaging (radiology and histopathology), and multi-omics signatures, alongside a critical comparison of the deep-learning architectures and integrative frameworks used to combine these data sources into advanced predictive models. By using fusion strategies (early, late, intermediate, and hybrid), MAI models are able to bridge the gap between genotype and phenotype, uncovering biological interactions that remain invisible to single-modality analysis. Current applications demonstrate significant improvements in diagnostic sensitivity, automated tumor grading, and the prediction of complex clinical outcomes, such as immunotherapy response and overall survival, referencing leading-edge tools and frameworks currently used or in active research. However, the transition from research to clinical practice is hindered by limitations such as data fragmentation, demographic biases, limited model explainability, and evolving regulatory requirements. We further outline emerging directions, including multimodal foundation models, large language models, and retrieval-augmented, agent-based systems. We concluded that the convergence of multimodal data streams and biologically informed AI represents the essential step for the next generation of personalized cancer care.
