This paper reviews the applications and challenges of artificial intelligence (AI) in the diagnosis and management of lung nodules and lung cancer.
AI demonstrates high accuracy in nodule detection, characterization, and risk stratification, contributing to improved radiology workflow efficiency. In low-dose CT (LDCT) screening, AI-based computer-aided detection (CAD) systems can increase detection sensitivity while reducing false positives.
Multiple commercial AI solutions, including Aview LCS (Coreline Soft), are already being applied in clinical practice.
However, several challenges remain for broader adoption, including regulatory approval, data privacy, algorithm bias, and integration into clinical workflows. Future directions include multimodal data integration and the advancement of personalized medicine.
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