肺癌长期位居全球恶性肿瘤死亡首位，在我国尤为突出。低剂量CT筛查显著提高了早期肺癌检出率，但同时也带来了假阳性增加、过度干预、随访困难及区域医疗差异等现实问题。近年来，人工智能（artificial intelligence，AI）、大语言模型、医疗物联网及元宇宙医学快速发展，使医学智能体逐渐由单纯影像辅助工具演变为具备感知、推理、决策、执行、反馈与持续学习能力的新型数字医疗主体。肺结节智能体是基于多模态数据融合、医学GPT、知识图谱及连续数字医疗体系构建的新型数字医学系统，可围绕肺结节完成风险识别、动态分层、路径推荐、长期随访及闭环管理，从而实现肺癌风险的全流程精准控制。当前，AI已从传统计算机辅助检测逐渐发展至多智能体协同体系，并开始进入数字化MDT、居家医院、数字孪生及元宇宙医学等更深层场景。本文结合国内外研究、共识指南及白春学教授团队提出的BAIMGPT与PNapp 5A体系，系统综述肺结节智能体的概念、技术基础、核心架构、临床应用、现实挑战及未来发展方向，重点讨论 AI在肺结节检出、风险分层、多模态融合、连续随访、赋能基层及真实世界治理中的作用。研究提示，肺结节智能体真正管理的对象，并非“影像中的结节”，而是患者未来发生肺癌的动态风险。将来，智能体有望推动肺结节管理从“发现结节”进入“精准管理风险”时代，实现“名医治未病，元医惠众生”愿景。

Lung cancer remains the leading cause of cancer-related mortality worldwide and poses an especially severe burden in China. Low-dose computed tomography (LDCT) screening has significantly improved the detection rate of early-stage lung cancer; however, it has also introduced major challenges, including increased false-positive findings, overtreatment, difficulties in long-term follow-up, and regional disparities in healthcare resources. In recent years, the rapid development of artificial intelligence (AI), large language models (LLMs), the medical Internet of Things (MIoT), and Metaverse Medicine has driven the evolution of Medical AI Agents from simple imaging-assistance tools into novel digital medical entities capable of perception, reasoning, decision-making, execution, feedback, and continuous learning. Pulmonary Nodule Agents represent a new generation of digital medical systems built upon multimodal data integration, medical GPT technologies, knowledge graphs, and continuous digital healthcare frameworks. These systems can perform risk identification, dynamic stratification, pathway recommendation, long-term follow-up, and closed-loop management for pulmonary nodules, thereby enabling precision control of lung cancer risk throughout the entire clinical pathway. Currently, AI has evolved from traditional computer-aided detection (CAD) systems toward multi-agent collaborative architectures and is increasingly being integrated into advanced clinical scenarios, including digital multidisciplinary team (MDT) management, Hospital at Home, digital twins, and Metaverse Medicine. Drawing upon international research, consensus guidelines, and the BAIMGPT and PNapp 5A framework proposed by Professor Chunxue Bai's team, this article systematically reviews the concepts, technological foundations, core architectures, clinical applications, real-world challenges, and future development trends of pulmonary nodule agents. Particular emphasis is placed on the role of AI in pulmonary nodule detection, risk stratification, multimodal integration, continuous follow-up, grassroots healthcare empowerment, and real-world governance. Evidence suggests that pulmonary nodule agents are not merely managing "nodules on imaging," but rather the dynamic future risk of lung cancer in individual patients. In the future, such intelligent agents are expected to transform pulmonary nodule management from a paradigm of "detecting nodules" to one of "precision risk management," ultimately advancing the vision of "preventive medicine by renowned physicians and universal healthcare enabled by metaverse medicine."