AI-Powered Predictive Models For Cancer Detection: Advancing Radiology Analysis In Breast And Lung Cancer Diagnosis

Cancer functions as a top-ranking disease which leads to deaths worldwide, yet early precise detection means vital elements to enhance survival rates among patients. The current traditional diagnostic methods, which depend on radiology-based approaches such as mammography and computed tomography (CT), require human interpretation that suffers from errors and variability. The accuracy and efficiency of cancer detection have improved much through recent advancements in artificial intelligence (AI), mainly due to deep learning and machine learning techniques. Combining AI predictive models, particularly CNNs and transformer-based and hybrid models, provides effective breast and lung cancer detection through automated radiology analysis with superior performance. This paper investigates artificial intelligence systems that analyze mammograms, X-ray images, and CT scans for better diagnostic accuracy and precision. The evaluation focuses on multiple AI algorithms, their training datasets, and their precise identity of cancerous lesions. The research extensively analyses clinical application problems, including data heterogeneity and model bias, and explains ability and regulatory compliance challenges. The discussion consists of AI implementation within the healthcare construct and demonstrates its capability to detect cancers early while cutting down diagnosis time and optimizing patient care. Different AI-based methodologies experience evaluation through a comparison test, which shows their success rate in detecting cancer while decreasing incorrect positive and negative results. Researchers demonstrate through their findings that artificial intelligence transforms radiology by establishing novel screening approaches that are faster, more efficient, and more personalized. AI solutions implemented in oncology have futuristic potential due to their ability to facilitate early diagnosis, which leads to superior patient results.