Advances in Artificial Intelligence for Lung Cancer Detection: A Review of Imaging and Computational Approaches

Abstract

Lung cancer is the leading cause of cancer-related deaths worldwide, and early detection significantly improves survival. Artificial intelligence (AI), particularly deep learning and convolutional neural networks (CNNs), has emerged as a powerful tool in thoracic imaging and diagnostics. Recent research has demonstrated that AI is capable of accurately detecting, categorizing, and segmenting lung nodules on chest radiographs, low-dose CT (LDCT), and histology, often matching or outperforming radiologists in this regard. AI also makes it easier to predict histological subtypes, characterize non-invasive tumors, and integrate prognostic information with clinical data. However, problems like limited dataset generalizability, high false-positive rates, and restricted clinical