Harnessing Artificial Intelligence for Precision Diagnosis and Treatment of Triple Negative Breast Cancer

Triple-Negative Breast Cancer (TNBC) is a highly aggressive subtype of breast cancer (BC) characterized by the absence of estrogen, progesterone, and HER2 receptors, resulting in limited therapeutic options. This article critically examines the role of Artificial Intelligence (AI) in enhancing the diagnosis and treatment of TNBC treatment. We begin by discussing the incidence of TNBC and the fundamentals of precision medicine, emphasizing the need for innovative diagnostic and therapeutic approaches. Current diagnostic methods, including conventional imaging techniques and histopathological assessments, exhibit limitations such as delayed diagnosis and interpretative discrepancies. This article highlights AI-driven advancements in image analysis, biomarker discovery, and the integration of multi-omics data, leading to enhanced precision and efficiency in diagnosis and treatment. In treatment, AI facilitates personalized therapeutic strategies, accelerates drug discovery, and enables real-time monitoring of patient responses. However, challenges persist, including issues related to data quality, model interpretability, and the societal impact of AI implementation. In the conclusion, we discuss the future prospects of integrating AI into clinical practice and emphasize the importance of multidisciplinary collaboration. This review aims to outline key trends and provide recommendations for utilizing AI to improve TNBC management outcomes, while highlighting the need for further research.