Recent Advances In Breast Cancer And Role Of Artificial Intelligence In Prediction And Diagnosis

Abstract

Breast cancer is still the most common cancer in women around the world, but research in biology, screening, and treatment is moving quickly. In the last five years, progress in molecular biology (like genomic profiling and the development of targeted therapies) and detection technologies (like 3D imaging) have made care more personalized and improved outcomes. At the same time, artificial intelligence (AI), which includes traditional machine learning, deep learning, and radiomics, has become a powerful tool for predicting and diagnosing breast cancer. AI can find patterns in large imaging and molecular datasets that are too small for people to see. AI-driven algorithms have been shown to work as well as or better than human experts in breast imaging techniques like mammography, ultrasound, MRI, digital breast tomosynthesis, and thermography, as well as in digital pathology (1,2). We look at these changes from both a global and an Indian point of view: India has a lot of problems because the disease starts at a young age. This has led to new screening methods like AI-enabled thermography. We also talk about issues with data quality, how to make it fit into clinical workflow, ethics and regulations, and we give an overview of where we want to go in the future with clinical deployment. This review is set up to talk about (1) new developments in breast cancer biology and treatment, (2) traditional and AI-enabled detection and screening, (3) how AI is used in certain imaging and pathology methods, (4) how AI compares to traditional methods, (5) problems and future directions, with a focus on global statistics and the Indian experience