Integrating AI-Based Digital Pathology with Spatial Transcriptomics for Deeper Insight

Histopathology preserves tissue architecture, whereas spatial transcriptomics measures gene expression within its native spatial context. Integrating these modalities with artificial intelligence (AI) can link morphology to spatial molecular states and scale spatial discoveries to routine pathology cohorts. Early hematoxylin and eosin (H&E)-to-spatial models demonstrated the feasibility of local gene-expression prediction. However, transcriptome-wide reconstruction remains constrained by gene-specific predictability, molecular sparsity, image quality, platform heterogeneity, and limited generalization. Xenium, CosMx, and related technologies are moving molecular supervision from multicellular spots toward cellular and subcellular units. In parallel, OmiCLIP exemplifies the shift from task-specific regression toward reusable visual-omics representations. We review these experimental and computational transitions and their applications in tumor microenvironment (TME) analysis, biomarker discovery, prognosis, and treatment-response prediction. We argue that the most credible translational goal is not to infer every transcript from H&E, but to identify biologically coherent spatial states with reproducible morphological correlates. Predictive uncertainty should be quantified, and these states should be validated across independent platforms and cohorts before being mapped across large archival collections. Future progress should extend this framework beyond static two-dimensional transcriptomic maps toward spatial multi-omics, longitudinal morphomolecular trajectory prediction, and three-dimensional tissue modeling.