An Imaging Perspective on AI's Function in the Lung Cancer Diagnosis and Detection

Abstract

Due in large part to late diagnosis, and the primary cause of cancer-related death globally is still lung cancer, primarily as a result of late detection and ineffective treatment at later stages. When it comes to lung cancer screening, detection, diagnosis, and staging, medical imaging is essential; yet, Growing data volumes, inter-observer heterogeneity, and the modest presence of early-stage disease pose challenges to conventional picture interpretation. Artificial intelligence (AI), which makes automated, quantitative, and repeatable analysis possible, has become a potent instrument in recent years to improve imaging-based lung cancer assessment.

The review provides an in-depth overview of the role of artificial intelligence in the diagnosis and detection of lung cancer through the imaging perspective. We discuss the way machine learning and deep learning methods are being applied to the primary imaging modalities, including computed tomography, low-dose CT, positron emission tomography-computed tomography, and chest radiography. We consider such significant AI-based application like TNM staging, segmentation, benign-malignant classification, pulmonary nodule recognition, and tumour characterisation. Radiomics and multimodal AI make personalised decision-making and non-invasive tumour characterisation possible. Also, the clinical effects of AI on the optimization of screening, workflow, diagnostic accuracy, and early detection are summarized in this paper. New solutions such as explainable AI and multimodal data integration are evaluated with seriousness and the limitations that exist today in terms of data heterogeneity, model generalizability, interpretability, and regulatory issues are also discussed. On balance, AI can dramatically transform the frame of imaging of lung cancer by helping to diagnose lung cancer earlier and make more precise clinical decisions. In order to realize the full clinical advantages of AI-based imaging in lung cancer treatment, clear models need to be developed, continuous validation is required, and the seamless integration into clinical pathways is needed.