中国P站

Abstract

Early detection of cancer in asymptomatic individuals is critical for improving patient outcomes and reducing mortality. Traditional cancer screening methods are often limited to single cancer types and can be invasive, costly, or lack sensitivity for early-stage tumors. Recent advances in artificial intelligence (AI) combined with multi-cancer blood test panels offer a promising approach for simultaneous, non-invasive detection of multiple cancers from a single blood sample. AI algorithms enhance the sensitivity and specificity of these tests by integrating complex biomarker data and personal risk factors, enabling precise cancer signal identification and tissue of origin prediction. This review discusses the principles, technological advances, and clinical implications of AI-optimized multi-cancer blood testing and risk modeling in asymptomatic populations. We also explore challenges, current applications, and future perspectives, highlighting their potential to transform cancer screening and personalize preventive care.