Application of spatial omics, multi-omics, and artificial intelligence in inflammatory bowel disease: Current applications and emerging opportunities

Table 1 Conceptual transition from conventional clinical and histopathology-based approaches toward next-generation molecular, spatial, and artificial intelligence frameworks in inflammatory bowel disease

Aspect	Traditional approaches	Emerging approaches
Disease definition	Defined by clinical presentation and histopathological features	Defined by molecular, cellular, and spatial disease states within tissue microenvironments[25-27]
Biomarkers	Detected through single serum or faecal biomarkers (e.g., calprotectin)	Integrated multi-layered molecular signatures across histopathology and omics[28-30]
Inflammation assessment	Analysis of endoscopic and histological scoring systems	Quantitative, spatially resolved inflammation mapped at cellular and subcellular resolution[31-33]
Disease prediction	Often, the disease is detected after the clinical onset	AI-enabled integration of multi-modal molecular, spatial, and clinical data to predict disease risk, progression, and outcomes before or early in the disease course[22-24]
Treatment decisions	Treatment choices depend on empirical evidence, incremental approaches, or a trial-and-error method of increasing therapy	Predictive, mechanism-informed therapy guided by molecular and spatial profiling[34-36]
Clinical trials	Mostly rely on animal models and late-stage human testing	Development and advancement of human-relevant non-animal platforms, including intestinal organoids, gut-on-chip systems, and in silico simulations enabling virtual or hybrid clinical trials in IBD[37-39]

IBD: Inflammatory bowel disease; AI: Artificial intelligence.