Chin Med J (Engl). 2025 Nov 28;138(24):3332-50. doi: 10.1097/CM9.0000000000003922. Online ahead of print.

ABSTRACT

Personalized medicine for gastric cancer continues to face numerous challenges, primarily due to the complexity of clinical decision making and the difficulty of integrating multimodal data. Artificial intelligence (AI), with its powerful capabilities in feature learning and pattern recognition, is emerging as a key technology to overcome these barriers. It provides critical support in areas such as early screening, histological subtyping, prediction of treatment response, and prognostic risk stratification. This review examines the application of AI in diagnosing and treating gastric cancer, with particular attention to the current mainstream AI methodologies, including feature engineering and deep learning and the rapidly evolving pretrained foundation models and multimodal large models. With the integration of medical images, digital pathology, multiomics data, and structured clinical information, AI systems are increasingly effective at capturing tumor heterogeneity and supporting complex clinical decisions in real time. On the one hand, task-specific models have demonstrated excellent performance in subtyping, staging, and prognosis assessment. On the other hand, the rise of foundation models and general-purpose large models is redefining the limits of AI in cross-task transfer, complex reasoning, and human-machine interaction. These technologies hold promise in addressing key obstacles such as data scarcity, modality heterogeneity, and fragmented clinical workflows, offering a feasible path toward a unified and efficient AI-driven diagnostic and therapeutic system for gastric cancer. As technological maturity progresses alongside the development of robust safety and ethical frameworks, AI is expected to evolve from a static auxiliary interpretation tool into an intelligent decision-making platform capable of semantic understanding, dynamic feedback, and multidisciplinary collaboration-therefore playing a pivotal role across the full spectrum of precision medicine in gastric cancer.

PMID:41400327 | PMC:PMC12721780 | DOI:10.1097/CM9.0000000000003922