Lung Cancer in the New Era: Trends, Innovations, and Future Recommendations

Abstract

ABSTRACT Lung cancer remains one of the most serious global health concerns, with patient survival influenced by lifestyle habits, genetic makeup, and environmental conditions. This paper reviews how new advances in artificial intelligence (AI) and machine learning (ML) are changing the way lung cancer is detected, classified, and treated. Modern deep learning (DL) methods, especially convolutional neural networks (CNNs), have shown stronger performance than many traditional diagnostic techniques, particularly when analyzing medical scans such as low‐dose computed tomography (CT) images. These approaches not only support early cancer detection but also enable more precise classification of its subtypes, leading to more reliable outcomes. In addition, AI is increasingly used to design treatment plans tailored to individual patients' needs, taking genetic variations into account. This makes therapies more effective and reduces unnecessary side effects. Despite these advances, several barriers still limit clinical adoption, including differences in available data, legal and regulatory requirements, and privacy‐related ethical issues. By also integrating environmental risk factors, such as long‐term exposure to air pollution, AI systems may more effectively identify high‐risk groups. The review suggests ways to address these barriers and highlights the growing potential of AI to reshape lung cancer care. This article is categorized under: Technologies > Computational Intelligence Application Areas > Health Care Algorithmic Development > Biological Data Mining

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