The advancements in artificial intelligence towards the diagnosis of lung cancer

Lung cancer remains the leading cause of cancer-related mortality worldwide, with delayed diagnosis being a major contributor to poor outcomes. Artificial intelligence (AI) has emerged as a transformative tool for enhancing diagnostic accuracy, improving early detection, and enabling personalized treatment strategies. This non-systematic review evaluates current advancements in AI for lung cancer diagnosis across imaging, pathology, and integrative risk stratification. AI-assisted radiological tools, including deep learning models applied to chest X-rays, CT scans, PET/CT, and thoracoscopic images, have demonstrated superior sensitivity and specificity in detecting pulmonary nodules and staging disease compared to conventional radiology. In pathology, AI applications in histological classification, biomarker detection, and prediction of tumor invasiveness have significantly improved diagnostic precision and reproducibility, supporting tailored therapeutic decision-making. Integrative multimodal AI models that combine imaging, clinical, and genomic data have further enhanced prognostic assessment and risk stratification. Despite these advancements, challenges remain regarding data availability, interpretability, ethical concerns, and legal implications of AI autonomy in clinical practice. Future progress will require explainable, transparent, and prospectively validated AI systems developed in close collaboration between clinicians and technology experts. Overall, AI holds the potential to revolutionize lung cancer diagnostics by enabling earlier detection, personalized care, and improved patient outcomes.