Artificial intelligence-assisted point-of-care devices for lung cancer

Lung cancer is the leading cause of cancer-related deaths worldwide, primarily due to late-stage detection, which limits treatment options. Early detection and screening can increase survival rates, but traditional medical imaging methods are costly and inconvenient. Point-of-care biosensors present a promising alternative, being userfriendly, less labor-intensive, and minimally invasive. With high sensitivity and selectivity, these biosensors detect lung cancer-associated biomarkers, including protein and nucleic acid, in biological fluids such as serum, urine, and saliva. Integrating artificial intelligence (AI) with biosensors has further improved their performance. AI algorithms can analyze complex data, differentiate lung cancer patients from healthy individuals, and even predict the risk of cancer metastasis. Despite these advancements, a comprehensive review of AI-coupled biosensors for lung cancer screening and detection has not yet been conducted. The clinical translation of these biosensors is challenged by a lack of standardization in biomarker selection, the number of biomarkers tested, and the determination of clinical cut-off values. This review focuses on recent advances in biosensors for lung cancer screening and detection, the challenges in their clinical application, and the role of AI in improving biosensor performance. Additionally, it explores future perspectives on the evolution of AI-assisted biosensors into comprehensive health monitoring systems, aiming to bridge the gap between technological innovation and practical clinical use.