Revolutionizing Breast Cancer Treatment: Harnessing the Power of Artificial Intelligence in Overcoming Drug Resistance

Drug resistance is one of the major challenges in the treatment of breast cancer (BC) and contributes to its high mortality rate. Detecting breast cancer at an early stage, before the cells have formed subpopulations which can reduce heterogeneous molecular features, could mitigate the development of drug resistance, and increase the efficacy of treatment. Several artificial intelligence (AI)-based models and tools that are integrated with multi-omics data (genomics, epigenetics, proteomics, and metabolomics) are available for drug discovery, drug design using AI-based database repositories and de novo drug design through AI-based algorithms, including deep reinforcement learning, variational auto-encoders, recurrent neural network, and generative adversarial network. As more data becomes available, the capacity of these algorithms improves to make them iteratively more accurate, timely, and precise. By integrating AI into predictive models, healthcare professionals can gain valuable insights into the mechanisms of drug resistance and develop personalized treatment strategies to overcome treatment failure in BC. This chapter highlights the revolutionary impact of AI in addressing early detection of BC, drug discovery and design, overcoming drug resistance, improving treatment outcomes, and paving the way for precision medicine in BC.