Multimodal artificial intelligence integrates imaging, endoscopic, and omics data for intelligent decision-making in individualized gastrointestinal tumor treatment

Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity. Multimodal artificial intelligence (AI) addresses this challenge by integrating diverse data sources-including computed tomography (CT), magnetic resonance imaging (MRI), endoscopic imaging, and genomic profiles-to enable intelligent decision-making for individualized therapy. This approach leverages AI algorithms to fuse imaging, endoscopic, and omics data, facilitating comprehensive characterization of tumor biology, prediction of treatment response, and optimization of therapeutic strategies. By combining CT and MRI for structural assessment, endoscopic data for real-time visual inspection, and genomic information for molecular profiling, multimodal AI enhances the accuracy of patient stratification and treatment personalization. The clinical implementation of this technology demonstrates potential for improving patient outcomes, advancing precision oncology, and supporting individualized care in gastrointestinal cancers. Ultimately, multimodal AI serves as a transformative tool in oncology, bridging data integration with clinical application to effectively tailor therapies.