Journal of Basic and Applied Research International

Haematoxylin-and-eosin (H&E) histology remains the visual backbone of tissue diagnosis, yet routine morphology is only a partial projection of the molecular and cellular states that generate it. Spatial transcriptomics, multiplexed spatial proteomics and related spatial molecular technologies now provide a means to test which latent biological programmes are genuinely coupled to microscopic architecture, while artificial intelligence (AI) can learn those cross-modal relationships at scales inaccessible to unaided visual assessment. This critical narrative review examines how spatial omics and AI are changing the interpretation of H&E sections from descriptive morphology towards spatially grounded molecular phenotyping. Literature published from 1 January 2015 to 3 July 2026 was appraised, with earlier or adjacent evidence considered only when necessary to establish methodological context. The strongest evidence shows that histology contains reproducible signals of cellular composition, proliferation, immune organisation, stromal state and selected molecular programmes, and that paired spatial omics can localise and validate those associations. However, predictive success is uneven across genes, tissues and acquisition settings. Spatial autocorrelation, mixed-cell capture spots, section-registration error, library-size effects, stain and scanner variation, gene-selection bias and patient-level data leakage can create apparently convincing spatial predictions without establishing cell-intrinsic molecular inference. Recent foundation models improve representation quality and data efficiency, but their scale does not remove the need for molecular ground truth, external validation or uncertainty calibration. The most defensible near-term role for virtual spatial omics is therefore not replacement of definitive assays, but triage, cohort enrichment, hypothesis generation and targeted confirmation. Progress towards clinical deployment will require patient-level and institution-level validation, explicit morphology-to-molecule identifiability limits, spatially appropriate metrics, prospective workflow studies and models trained against high-resolution multimodal reference data. Spatial omics is most valuable not because it makes H&E obsolete, but because it provides the reference layer needed to determine what H&E truly contains and what AI merely appears to infer.