[Artificial intelligence-enabled advances in gastric cancer imaging: current status, challenges, and prospective strategies].

Gastric cancer remains one of the most common malignancies in China, and early diagnosis and accurate staging are critical for improving patient prognosis. Conventional imaging-based diagnosis of gastric cancer is limited by physician experience, equipment variability, and subjective interpretation, resulting in insufficient detection of early lesions and inconsistent staging assessment. Artificial intelligence (AI), powered by deep learning, radiomics, and related technologies, has demonstrated substantial potential in lesion detection, staging assessment, treatment response evaluation, and prognostic prediction for gastric cancer. These advances may improve diagnostic performance and promote more homogeneous clinical practice. However, clinical translation remains challenged by data heterogeneity and data silos, limited model generalizability, insufficient interpretability, inadequate prospective clinical evidence and regulatory frameworks, and variable acceptance among clinicians. This article reviews the current applications and technical advances of AI in gastric cancer imaging, analyzes the key barriers to clinical implementation, and proposes practical strategies from four perspectives: data governance, technological innovation, clinical translation, and physician-AI collaboration. The aim is to provide a reference for promoting standardized, accessible, and clinically meaningful implementation of AI in precision diagnosis and treatment of gastric cancer.