Artificial Intelligence in Radiology for Early Cancer Detection and Personalized Oncology Care

To review the state of artificial intelligence applications in radiology for early cancer detection and personalized oncology, focusing on recent (2021–2026) evidence from peer-reviewed studies, trials, and regulatory guidance. The Method We conducted a structured literature search in PubMed, IEEE Xplore, and relevant conference proceedings using terms like “AI radiology cancer screening,” “deep learning oncology imaging,” and “radiomics genomics.” We prioritized high-impact journals, systematic reviews, clinical trials, FDA/EMA approvals, and professional society guidelines. Key sections cover AI techniques, imaging modalities, performance metrics, case studies, genomics integration, workflow, ethics, economics, and future directions. Figures and tables summarize models, milestones, and workflows. The result we got AI techniques such as convolutional neural networks (CNNs), U-Nets for segmentation, transformer-based models, and radiomics-based machine learning have enabled detection of early cancer lesions across modalities. Mammography AI tools (e.g. Mirai, Lunit) and lung CT AI systems (e.g. deep 3D CNNs) have shown high sensitivity/specificity in trials. Integrating AI with genomics (radiogenomics) is emerging; for example, AI-derived radiomic features combined with gene signatures improve prognostication. Clinical validation often uses retrospective cohorts or reader studies; prospective trials are few but growing. Regulatory bodies (FDA, EMA) have cleared hundreds of AI devices and issued guidelines for AI/ML software life cycle. Economic models indicate AI can save costs in screening programs, especially when AI augments rather than replaces clinicians. And the Conslusion is AI in radiology shows great promise for more accurate and personalized cancer detection, but real-world implementation requires rigorous validation, regulatory oversight, and attention to bias and workflow integration. Further clinical trials, multi-center data sharing, and standards (e.g. CONSORT-AI) are needed. This review provides a comprehensive overview of progress and challenges in AI-enabled cancer imaging as of 2026.