Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges

Figure 1 Artificial intelligence-assisted diagnostic and clinical decision integration workflow for gastrointestinal cancers. Step 1: Input data layer: Integration of multi-dimensional data including molecular biomarkers (circulating tumor DNA, microsatellite instability status, CpG island methylation), endoscopic features (microstructural patterns, vascular abnormalities), and clinical parameters (tumor location). Step 2: Macroscopic observation layer: Visual identification of gastrointestinal lesion regions to facilitate subsequent artificial intelligence (AI) processing. Step 3: AI model processing layer: Gastric cancer analysis: Multimodal model incorporating ulcer-free status, high differentiation, and proximal tumor location [negative predictive value (NPV) = 100%] supports function-preserving surgical decisions. Colorectal cancer analysis: Vision Transformer-based model (ViTCol, NPV = 96%) combines 7 indicators including age and tumor location for precise lymph node metastasis risk stratification. Step 4: Clinical decision layer: AI-generated risk stratification guides therapeutic recommendations: Surgical resection for high-risk cases and endoscopic resection for low-risk cases. ctDNA: Circulating tumor DNA; MSI: Microsatellite instability; AI: Artificial intelligence; NPV: Negative predictive value; EGC: Early gastric cancer; NPV: Negative predictive value; ViT: Vision Transformer; LNM: Lymph node metastasis.