肝癌作为全球高发恶性肿瘤，其复杂的解剖结构与临床特征给微创外科治疗带来诸多限制。人工智能（AI）技术的崛起为突破这一困境提供了革命性方案。本文系统梳理AI在肝癌微创外科术前规划、术中导航及术后管理全流程的应用现状：术前阶段，AI通过多模态数据整合实现肝脏三维重建、手术风险评估与肿瘤生物学行为（如微血管侵犯）预测，为个体化手术方案制定提供量化依据；术中环节，AI增强现实导航、实时解剖结构识别与风险预警技术，显著提升了手术精准度与安全性，同时优化手术流程以促进操作标准化；术后管理中，AI模型整合多维度数据构建高精度预后预测与并发症风险评估系统，结合智能随访平台，实现复发风险分层与全程个体化管理。尽管AI技术展现出巨大潜力，但数据质量与标准化不足、模型泛化能力有限、临床集成障碍、决策可解释性欠缺及数据隐私安全问题仍制约其规模化临床转化。未来，随着多模态数据融合、联邦学习及可解释AI等技术的发展，AI有望推动肝癌微创外科向标准化、智能化及普惠化方向迈进，最终实现精准医疗目标。

Liver cancer, as a globally prevalent malignant tumor, poses significant challenges to minimally invasive surgical treatment due to its complex anatomical structure and clinical characteristics. The rise of artificial intelligence (AI) technology offers a revolutionary solution to this predicament. This article systematically reviews the current application status of AI in the entire process of minimally invasive surgery for liver cancer, including preoperative planning, intraoperative navigation, and postoperative management. In the preoperative stage, AI integrates multimodal data to achieve three-dimensional liver reconstruction, surgical risk assessment, and prediction of tumor biological behavior (such as microvascular invasion), providing a quantitative basis for the formulation of individualized surgical plans. During the intraoperative phase, AI technologies such as augmented reality navigation, real-time anatomical structure recognition, and risk warning significantly enhance surgical precision and safety, while optimizing the surgical process to promote standardization. In postoperative management, AI models integrate multi-dimensional data to build high-precision prognosis prediction and complication risk assessment systems, combined with intelligent follow-up platforms, to achieve stratification of recurrence risk and full-course individualized management. Despite the significant potential of AI technology, issues such as insufficient data quality and standardization, limited model generalization ability, clinical integration obstacles, lack of decision interpretability, and data privacy and security concerns still restrict its large-scale clinical transformation. In the future, with the development of multimodal data fusion, federated learning, and explainable AI, AI is expected to drive minimally invasive liver surgery towards standardization, intelligence, and universalization, ultimately achieving the goal of precision medicine.