The integration of artificial intelligence (AI) in medical diagnostics represents a significant advancement in managing upper gastrointestinal (GI) cancer, a major cause of global cancer mortality. Specifically for gastric cancer (GC), chronic inflammation causes changes in the mucosa such as atrophy, intestinal metaplasia (IM), dysplasia and ultimately cancer. Early detection through endoscopic regular surveillance is essential for better outcomes. Foundation models (FM), which are machine or deep learning models trained on diverse data and applicable to broad use cases, offer a promising solution to enhance the accuracy of endoscopy and its subsequent pathology image analysis. This review explores the recent advancements, applications, and challenges associated with FM in endoscopy and pathology imaging. We started by elucidating the core principles and architectures underlying these models, including their training methodologies and the pivotal role of large-scale data in developing their predictive capabilities. Moreover, this work discusses emerging trends and future research directions, emphasizing the integration of multimodal data, the development of more robust and equitable models, and the potential for real-time diagnostic support. This review aims to provide a roadmap for researchers and practitioners in navigating the complexities of incorporating FM into clinical practice for prevention/management of GC cases, thereby improving patient outcomes.

翻译：人工智能在医学诊断中的整合代表了上消化道癌症管理领域的重大进展，而上消化道癌症是全球癌症死亡的主要原因之一。具体到胃癌，慢性炎症会导致黏膜发生一系列变化，如萎缩、肠上皮化生、异型增生，并最终发展为癌症。通过内窥镜定期监测进行早期发现对于改善预后至关重要。基础模型是一种在多样化数据上训练、可适用于广泛使用场景的机器学习或深度学习模型，它为提升内窥镜检查及其后续病理图像分析的准确性提供了极具前景的解决方案。本综述探讨了基础模型在内窥镜与病理成像领域的最新进展、应用与挑战。我们首先阐释了这些模型的核心原理与架构，包括其训练方法以及大规模数据在开发其预测能力中的关键作用。此外，本文讨论了新兴趋势与未来研究方向，重点强调多模态数据的整合、开发更稳健且公平的模型，以及实现实时诊断支持的潜力。本综述旨在为研究人员和实践者提供一份路线图，以指导他们将基础模型整合到临床实践中用于胃癌病例的预防与管理，从而改善患者预后。