This review thoroughly investigates the application and advancements of artificial intelligence (AI) technology in the comprehensive management of lung cancer. AI is utilized at various stages of lung cancer diagnosis and treatment through techniques such as computer vision, deep learning, and natural language processing. In the early diagnosis stage, AI assists in identifying high-risk populations and, in conjunction with pathological techniques, accomplishes functions like histological classification of lung cancer tissues, prediction of molecular markers, and quantitative analysis of immunohistochemistry. During the treatment stage, AI integrates multimodal data to aid in formulating individualized treatment plans and enhances efficiency via clinical decision support systems (CDSS). In the follow-up stage, continuous patient monitoring and optimization of follow-up strategies are realized through imaging, remote monitoring, and intelligent follow-up systems. The prospects for AI medical technology are promising. However, it still confronts challenges such as weak generalizability, poor interpretability of AI decisions, and ethical and legal issues.
