肺部疾病长期居全球死亡与致残前列，胸片与 CT 虽是筛查、诊断与随访的基础入口，但在高负荷与复杂病谱下暴露出漏诊、误诊及定量不足等局限。深度学习、影像组学与多模态大模型的兴起，使人工智能（artificial intelligence，AI ）成为胸部影像从“读片工具”迈向“系统工程”的关键驱动力。AI 已在肺结节/肺癌、结核、肺炎、间质性肺疾病（interstitial lung  disease， ILD）、慢性阻塞性肺疾病（chronic obstructive pulmonary disease， COPD）、小气道及肺血管疾病等多谱系任务中显著提升检测、分割、表型量化与风险预测能力，并稳定量化倍增时间、纤维化负荷和气道重塑等关键指标，成为落实 Fleischner、亚洲及中国指南的重要技术基础。在预防与筛查中，AI 支持高危人群识别、胸片大规模筛查、低剂量CT（low-dose computed  tomography， LDCT）风险分层及间质性肺异常（interstitial lung abnormality， ILA）、小气道病等亚临床异常的早期发现，可与健康管理、数字孪生和元宇宙平台结合，构建防线前移式干预模式。在诊断中，影像大模型与医学 GPT 可生成结构化报告，提供 “指南在线”的决策支持，并面向医生与患者输出差异化解释；在治疗与随访中，AI 赋能放疗计划、术前导航、治疗反应预测及肺功能估测，推进影像—功能一体化和个体化长期管理。未来将聚焦通用胸部影像大模型构建、5P 医学深度融合、联邦学习 与全球协同数据网络建设，并从“影像环节智能化”走向“医院—社区—家庭—云端—元宇宙”贯通的全病程系统工程，使胸片与CT成为数字呼吸健康生态的关键基础设施。

Lung diseases have long been at the forefront of global mortality and disability, although chest X-ray and CT are the  basic entrances for screening, diagnosis and follow-up, they are exposed to limitations such as miss diagnosis, misdiagnosis and  insufficient quantification under high load and complex disease spectrum. The rise of deep learning, radiomics, and multimodal large  models has made Artificial Intelligence (AI) a key driving force for chest images to move from "reading tools" to "system engineering". AI has significantly improved detection, segmentation, phenotypic quantification, and risk prediction capabilities in multi-spectrum  tasks such as lung nodules/lung cancer, tuberculosis, pneumonia, interstitial lung disease (ILD), chronic obstructive pulmonary disease  (COPD), small airways, and pulmonary vascular diseases, and has stabilized key indicators such as doubling time, fibrosis burden, and  airway remodeling, becoming an important technical basis for the implementation of Fleischner, American College of Chest Physicians  (ACCP), and China guidelines. In prevention and screening, AI supports the identification of high-risk groups, large-scale chest X-ray  screening, LDCT risk stratification, and early detection of subclinical abnormalities such as ILA and small airway disease, which can  be combined with health management, digital twins, and metaverse platforms to build a forward-moving defense line intervention  model. Physicians and patients generate structured reports, provide "guide online" decision support, and output differentiated  explanations by using imaging diagnostic models and medical GPTs. AI also empowers radiotherapy planning, preoperative navigation, treatment response prediction, and lung function estimation, promoting image-function integration and individualized long-term  management for treatment and follow-up. In the future, it will focus on the construction of general chest imaging large models, the deep integration of 5P medicine, the construction of federated learning and global collaborative data networks, and move from "intelligent  imaging links" to the whole course of the disease system project that connects "hospital-community-family-cloud-metaverse", so that  chest X-ray and CT will become the key infrastructure of the digital respiratory health ecosystem.