(untitled) (Physical Education, Health and Social Sciences, 2026)
The rapid advancement of artificial intelligence (AI), particularly deep learning architectures such as Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid models, has fundamentally transformed early cancer detection using medical imaging. This comprehensive review synthesizes recent developments in AI-based diagnostic models across major cancer types (breast, lung, prostate, pancreatic, and others), highlighting their superior or comparable performance to human radiologists in sensitivity, specificity, and early-stage lesion identification. Landmark prospective trials (MASAI study) and large-scale validations (e.g., Sybil model for lung cancer risk prediction) demonstrate that AI can significantly improve screening sensitivity, reduce inter-observer variability, and enable non-invasive radiogenomic phenotyping. Key innovations include multi-modal data fusion (radiomics + genomics), generative models (diffusion models for data augmentation and tumor progression simulation), foundation models for cross-modality learning, federated learning for privacy-preserving collaborative training, and explainable AI (XAI) techniques (heatmaps, SHAP, attention visualization) to build clinical trust. Despite these advances, persistent challenges remain, including workflow integration, alert fatigue, algorithmic bias, liability allocation, regulatory compliance (FDA clearances, EU AI Act), and reimbursement pathways. The paper concludes that AI is transitioning from an assistive tool to a core component of precision oncology, with future success dependent on robust external validation, bias mitigation, seamless PACS integration, and interdisciplinary collaboration between clinicians, data scientists, and regulators.