LAUSR

The global cancer burden continues to escalate, driven by a significant rise in new cases and cancer-related deaths. Early detection through effective screening programs is paramount for reducing mortality, and the integration of Artificial Intelligence (AI) into oncological imaging has shown transformative potential. This review comprehensively examines the evolution and clinical application of AI in oncological imaging for cancer detection across various modalities, including ultrasound, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and endoscopy, highlighting significant advancements in early cancer screening. We further address the challenges associated with AI implementation in medical imaging, including dataset bias, the need for robust regulatory frameworks, and technical integration barriers. Emphasis is placed on the necessity of standardized, diverse datasets, explainable algorithms, and equitable implementation to mitigate disparities. By aligning technological innovation with rigorous clinical validation, ethical governance, and seamless workflow integration, AI is poised to revolutionize cancer care through earlier and more accurate detection, personalized risk stratification, and ultimately, improved patient outcomes.