Lung cancer remains a leading cause of cancer-related mortality and generates large, heterogeneous datasets across imaging, pathology, and molecular profiling. While these data create real opportunities for precision oncology, they also increase cognitive and workflow burdens for clinicians. Artificial intelligence (AI) methods, including machine learning (ML), deep learning (DL) with convolutional neural networks (CNNs), natural language processing (NLP), and explainable approaches, have been explored across the lung cancer care pathway, from screening and diagnosis to risk stratification, treatment planning, and longitudinal monitoring. In this review, we synthesize current applications through a clinical translation lens, emphasizing not only reported performance metrics but also study design, dataset characteristics, and the strength of validation. Although several models show strong results in controlled settings, many remain retrospective, single-center, and lack robust external or prospective evaluation, which limits generalizability and clinical impact. We also discuss practical barriers to deployment, including data quality and interoperability, reproducibility, integration into clinical workflows, and ethical issues such as privacy, transparency, and accountability. Overall, the field is advancing rapidly, but meaningful clinical benefit will depend on rigorous validation, careful implementation, and sustained collaboration between clinicians, data scientists, and health systems.
Jiang et al. (Wed,) studied this question.