One scan, multiple insights: A review of AI-Driven biomarker imaging and composite measure detection in lung cancer screening

Abstract

In an era where early detection of diseases is paramount, integrating artificial intelligence (AI) into routine lung cancer screening offers a groundbreaking approach to simultaneously uncover multiple health conditions from a single scan. The fact that lung cancer is still the most common cause of cancer-related deaths globally emphasizes how important early detection is to raising survival rates. Traditional low dose computed tomography (LDCT) focuses primarily on identifying lung malignancies, often missing the opportunity to detect other clinically relevant biomarkers. This review explores the expanding role of AI in radiology, where AI-driven algorithms can simultaneously detect multiple biomarkers and composite health measures, facilitating the opportunistic identification of conditions beyond lung cancer. These include musculoskeletal disorders, cardiovascular diseases, pulmonary conditions, hepatic steatosis, and malignancies in the adrenal and thyroid glands, as well as breast tissue. Through an extensive review of current literature sourced from PubMed, the review highlights advancements in AI-driven biomarker detection, evaluates the potential benefits of a broader diagnostic approach, and addresses challenges related to model standardization and clinical integration. AI-enhanced LDCT screening shows significant promise in augmenting routine screenings, potentially advancing early detection, comprehensive patient assessments, and overall disease management across multiple health conditions. • AI-enhanced LDCT screening enables opportunistic biomarker and composite measure detection. • AI tools improve diagnostic accuracy in identifying multiple health conditions. • Challenges include standardising AI algorithms and the risk of overdiagnosis.

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