Artificial intelligence and machine learning-driven advancements in gastrointestinal cancer: Paving the way for precision medicine

Gastrointestinal (GI) cancers remain a leading cause of cancer-related morbidity and mortality worldwide. Artificial intelligence (AI), particularly machine learning and deep learning (DL), has shown promise in enhancing cancer detection, diagnosis, and prognostication. A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed, Web of Science, and Scopus. Search terms included "gastrointestinal cancer", "artificial intelligence", "machine learning", "deep learning", "radiomics", "multimodal detection" and "predictive modeling". Studies were included if they focused on clinically relevant AI applications in GI oncology. AI algorithms for GI cancer detection have achieved high performance across imaging modalities, with endoscopic DL systems reporting accuracies of 85%-97% for polyp detection and segmentation. Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92. Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists (78.9% vs 80.0%), though without incremental value when combined with human interpretation. Multimodal AI approaches integrating imaging, pathology, and clinical data show emerging potential for precision oncology. AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks, with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care. However, broader validation, integration into clinical workflows, and attention to ethical, legal, and social implications remain critical for widespread adoption.