Foundation Models in Gastroenterology: Emerging Applications and Future Directions

Foundation models are being investigated in gastroenterology because the specialty already generates multimodal data, including endoscopy videos, pathology slides, abdominal images, laboratory results, molecular profiles, and longitudinal clinical notes. This review examines where these models may be useful in emerging applications, where evidence remains limited, and what is required before wider clinical deployment. Large language models (LLMs) can support document organization, report generation, guideline retrieval, and patient communication, but fluent output can conceal errors. Endoscopic models are beginning to move beyond single-task polyp detection towards reusable visual pretraining across gastrointestinal sites and procedures. Pathology and radiology foundation models may assist with biomarker prediction, image segmentation, and tumor detection, although performance remains sensitive to staining, scanners, acquisition protocols, and case mix. The immediate goal is not to build a single model that covers the entire specialty. Rather, gastroenterology needs systems that are locally validated, auditable, privacy-aware, uncertainty-calibrated, and sufficiently focused to remain within clinical responsibility.