Abstract
A revolutionary development in contemporary healthcare, especially in oncology, is the application of artificial intelligence (AI) to breast cancer diagnosis. The use of AI-powered diagnostic tools in breast cancer screening programs is examined in detail in this article, which shows notable gains in workflow efficiency, accuracy, and early detection. Along with quantitative performance indicators and system reliability measures, it examines the technological framework, which includes deep learning architectures and integration workflows. Through a thorough analysis of data processing issues, fixes, and clinical implications, the article shows how AI enhances radiologists' skills without replacing human knowledge. It also discusses future technological advancements and scaling issues, emphasizing the possibility of integrating multi-modal imaging and improving predictive capacities. This approach makes a strong case for wider deployment across healthcare systems and is a blueprint for future AI integration in medical diagnostics.
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