Progress and prospects of AI intelligent agents empowering the diagnosis and treatment of pulmonary nodules

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.”

Key Words: artificial intelligence; intelligent agent; pulmonary nodule; early lung cancer screening; medical GPT; multimodal integration