Abstract

Missing data presents a persistent challenge in biomedical research. Data imputation techniques have evolved from single-modality approaches to multi-modal approaches, which show great promise for imputing one modality based on the availability of another. Recent advancements in large, pre-trained artificial intelligence (AI) models, known as foundation models, offer even more powerful solutions for data imputation. We introduce the concept of cross-modal data modeling, a methodology harnessing foundation models to impute missing data and also generate realistic synthetic samples. Multi-modal modeling empowers researchers to model complex interactions among diverse biomedical data types, including omics and imaging. This approach can illuminate how one modality influences another, facilitating in-silico exploration of disease mechanisms without the need for extensive and costly real-world data collection. We highlight ongoing efforts in multi-modal modeling in spatial omics, digital pathology and radiology, and anticipate its substantial contributions to understanding disease biology and enhancing healthcare practices.