Artificial Intelligence in Oncology: Advances in Cancer Diagnosis, Genomic Profiling, and Treatment Planning

Artificial intelligence (AI) has made significant advances in oncology, demonstrating excellent diagnostic accuracy in imaging, enhanced genetic interpretation, and decision-support capabilities for personalized therapy planning. Despite these advances, implementation in routine clinical practice remains limited and uneven. This review argues that the primary bottleneck is not algorithmic performance, but rather a persistent Clinical Embedding Gap, which disconnects model accuracy from real-world integration within clinical workflows, institutional infrastructure, incentive structures, and outcome-based validation frameworks. A structured narrative review was conducted. A systematic literature search was performed using PubMed/MEDLINE and Web of Science to identify relevant peer-reviewed literature published between 2015 and 2025. The findings suggest that AI systems are commonly performance-validated but insufficiently outcome-verified, which contributes to limited adoption. This gap must be bridged through workflow-native design, prospective clinical validation, interoperable digital infrastructure, and alignment of technological innovation with healthcare system readiness. Addressing these structural challenges is critical for advancing AI from experimental augmentation to integrated standard-of-care oncology practice.