随着肺癌精准医学不断发展，尤其是非小细胞肺癌（NSCLC）进入以病理亚型、驱动基因、免疫生物标志物及微小残留病灶（MRD）共同决定治疗路径的新时代，传统仅依赖形态学阅片的病理诊断模式已难以满足临床需求。AI与数字病理的兴起，使全视野数字切片（WSI）从静态玻片转变为可计算、可共享、可追溯的数据载体，并推动肿瘤区域识别、组织学分型、 肿瘤细胞比例评估、PD-L1定量判读、肿瘤微环境分析及潜在分子表型预测等任务进入智能化阶段。与此同时，驱动基因检测由少数靶点扩展至多基因组，液体活检和ctDNA为组织不足病例提供分子分型补充，MRD监测则推动肺癌管理由治疗前单次分层走向围治疗期动态风险评估。本文系统梳理 AI 辅助病理切片判读、驱动基因与PD-L1/TMB/ctDNA/MRD整合、数字病理与分子分型联动、精准医学场景下的数据标准化，以及外部验证不足、平台差异、可解释性、法规治理和系统割裂等现实挑战。简言之，AI+数字病理与分子检测的核心价值，不仅在于提升单一检测环节的效率与一致性，更在于构建面向肺癌全流程管理的智能伴随诊断体系，实现病理、分子、液体活检、MRD与临床决策的连续耦合。未来，该领域将从单任务模型迈向多模态基础模型，从静态伴随诊断迈向动态伴随诊断，并在平台化、区域化和生态化建设中，与医学GPT、数字医学专家及BAIMGPT等理念深度融合，推动肺癌精准诊疗进入数据融合驱动的新阶段。

With the rapid evolution of precision oncology, particularly in non-small cell lung cancer (NSCLC), therapeutic decision-making is increasingly shaped by histologic subtype, driver alterations, immune biomarkers, and minimal residual disease (MRD). Under this paradigm, conventional pathology based solely on morphologic interpretation is no longer sufficient for modern clinical needs. The integration of artificial intelligence (AI) and digital pathology has transformed whole-slide imaging (WSI) from static glass slides into computable, sharable, and traceable data objects, enabling automated tumor region detection, histologic classification, tumor cell proportion estimation, PD-L1 quantification, tumor microenvironment analysis, and even prediction of potential molecular phenotypes. In parallel, molecular testing has expanded from a limited number of actionable genes to broad multigene panels, while liquid biopsy and circulating tumor DNA (ctDNA) provide complementary options for molecular profiling when tissue is limited. MRD monitoring further shifts lung cancer management from one-time pretreatment stratification toward dynamic peri-treatment risk assessment. This review systematically summarizes the roles of AI-assisted pathology interpretation, the integration of driver mutations with PD-L1, TMB, ctDNA and MRD, the coupling of digital pathology with molecular subtyping, the importance of data standardization in precision medicine, and the major barriers to clinical translation, including insufficient external validation, platform heterogeneity, limited interpretability, regulatory concerns, and fragmented workflows. We argue that the true value of AI enabled digital pathology and molecular testing lies not merely in improving the accuracy or efficiency of individual diagnostic steps, but in establishing an intelligent companion diagnostic system spanning the entire continuum of lung cancer care. Such a system can continuously integrate pathology, molecular profiling, liquid biopsy, MRD surveillance, and clinical decision-making. Looking forward, the field is expected to evolve from single-task algorithms to multimodal foundation models, from static companion diagnostics to dynamic companion diagnostics, and from isolated laboratory tools to regionalized, platform-based intelligent ecosystems, ultimately promoting data-driven precision lung cancer care.