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

Table 3 Clinical applications of multimodal artificial intelligence in personalized gastrointestinal cancer therapy

Application area	Core function	Key technologies/data	Primary value
Intelligent diagnosis & staging	Early screening & precise staging: Enhances tumor identification and classification, predicts metastasis risk	Imaging data: CT, EUS, PET/CT; Omics data: Radiomics, genomics; Clinical data: EHR	Increases early detection rates, reduces missed diagnoses; enables more accurate preoperative staging to inform treatment decisions
Treatment optimization	Treatment response prediction: Guides the selection of surgery, radiotherapy, chemotherapy, and targeted/immunotherapy regimens	Multimodal fusion models: e.g., MuMo model; Data integration: Radiomics, genomics, immunomics, tumor microbiome	Accurately predicts efficacy, avoids unnecessary treatments; guides personalized medication (e.g., targeted drug combinations) to overcome drug resistance and improve response rates
Prognostic assessment & follow-up management	Risk stratification & recurrence prediction: Precisely assesses patient survival and recurrence risk. Dynamic follow-up management: Enables personalized long-term monitoring	Prognostic models: Integrate clinical, imaging, genomic data. Intelligent systems: Clinical Decision Support Systems, EHR analysis	Enables precise risk stratification to guide adjuvant therapy; improves follow-up efficiency, provides timely recurrence alerts, and optimizes resource allocation

CT: Computed tomography; EUS: Endoscopic ultrasound; PET/CT: Positron emission tomography/computed tomography; EHR: Electronic health record.