人工智能(artificial intelligence，AI)正在改变胃癌外科治疗的格局。以深度学习为代表的人工智能技术，依靠自身强大的多维数据挖掘和模式识别能力，在术前精准分期、隐匿性腹膜转移筛查、新辅助治疗效果预估、术中淋巴结清扫和功能保护、术后个体化辅助治疗决策等方面展现出重要价值。通过影像组学和深度学习的相互融合，使隐匿性腹膜转移的预测达到曲线下面积(area under the curve，AUC)0.92以上；术中AI结合吲哚菁绿荧光导航和三维重建技术，在实施根治性手术的同时为肿瘤引流淋巴结功能性保护提供新路径；而在辅助治疗上，AI可以从常规病理切片中高效识别微卫星高度不稳定(MSI-H)、EB病毒阳性、HER2表达等分子特性，从而帮助精准识别免疫或靶向治疗优势人群。然而，AI临床转化还存在高质量数据缺乏、模型可解释性差、前瞻性验证弱等问题。未来外科医师应该从临床问题定义者、高质量数据生产者的角度参与AI模型的构建与迭代，在多模态融合、联邦学习、可解释性算法等方面进行协同创新，使胃癌外科从经验驱动向数据驱动的精准微创治疗转变。

Artificial intelligence is reshaping the landscape of gastric cancer surgery. Deep learning-based technologies, with their powerful capabilities in multidimensional data mining and pattern recognition, have demonstrated significant value across key clinical scenarios, including preoperative accurate staging, screening of occult peritoneal metastasis, prediction of neoadjuvant therapy response, intraoperative lymph node dissection and functional preservation, and postoperative individualized adjuvant therapy decision-making. The integration of radiomics and deep learning has increased the area under the curve (AUC) for predicting occult peritoneal metastasis to over 0.92. Intraoperative AI combined with indocyanine green fluorescence navigation and three-dimensional reconstruction offers a novel approach to preserve tumor-draining lymph nodes (TDLNs) without compromising oncological radicality. In the field of adjuvant therapy, AI can efficiently identify molecular features such as microsatellite instability-high (MSI-H), Epstein-Barr virus positivity, and HER2 expression from routine histopathological sections, facilitating precise selection of patients who are most likely to benefit from immunotherapy and targeted therapy. However, clinical translation of AI still faces challenges, including a lack of high-quality datasets, insufficient model interpretability, and weak prospective validation. In the future, surgeons should engage deeply in the development and iteration of AI models as definers of clinical problems and producers of high-quality data, and promote innovation through multimodal integration, federated learning, and explainable algorithms, thereby driving gastric cancer surgery from an experience-driven discipline toward a new paradigm of data-driven precision minimally invasive surgery.