Abstract
Lung cancer remains the leading cause of cancer-related deaths globally. Over the past decade, the development of artificial intelligence (AI) has significantly propelled lung cancer care, particularly in areas such as lung cancer early diagnosis, survival prediction, recurrence prediction, medical image processing, medical image registration, medical visual question answering, clinical report writing, medical image generation, and multimodal integration. This review aims to provide a comprehensive summary of the various AI methods utilized in lung cancer care, with a particular emphasis on machine learning and deep learning techniques. Moreover, with the advent and widespread application of large language models (LLMs), vision language models (VLMs), and multimodal integration for downstream clinical tasks, we explore the current landscape these cutting-edge AI tools offer. However, it also presents both significant challenges and opportunities, including data privacy risks, inherent biases that may exacerbate healthcare disparities, model hallucinations, ethical implications, implementation costs, and the lack of standardized evaluation metrics. Furthermore, the translation of these technologies from experimental research to clinical implementation demands comprehensive validation protocols and multidisciplinary collaboration to guarantee patient safety, therapeutic efficacy, and equitable healthcare delivery. This review emphasizes the critical role of AI in enhancing our understanding and management of lung cancer, ultimately striving for precision medicine and equitable healthcare worldwide.