Current trends and future directions of artificial intelligence in lung cancer diagnosis

Lung cancer is the most lethal malignancy worldwide, largely due to its late detection after its progression to advanced stages. Over the last decade, artificial intelligence (AI) applications have shown significant potential in transforming lung cancer diagnostics by improving the speed, accuracy, and personalization of early detection strategies. This review provides a comprehensive overview of current AI application landscape in early lung cancer diagnosis, encompassing medical imaging, histopathology, liquid biopsy, natural language processing of electronic health records, and genomic profiling. We explain how machine learning, deep learning, and transformer-based models are employed in lung cancer diagnosis, and summarize recent cutting-edge advances, including multimodal AI platforms and Food and Drug Administration (FDA)-approved computer-aided diagnosis/detection (CAD) systems. Furthermore, we evaluate the challenges that impede clinical translation, including data heterogeneity, interpretability, and privacy, and present prospective directions such as federated learning and multi-omics integration. Through a comprehensive analysis of the dynamic evolution of AI applications in oncology, we aim to inform researchers, clinicians, and policymakers about its diagnostic potential and translational relevance in clinical practice.