Inteligencia artificial en el cáncer de mama: aplicaciones clínicas en diagnóstico, pronóstico y terapéutica

Breast cancer (BC) presents a considerable global health challenge and is characterized by increasing mortality and morbidity rates. Prompt screening and accurate diagnosis are crucial for improving patient outcomes. For the assessment of BC, radiographic imaging modalities such as digital breast tomosynthesis (DBT), ultrasound, digital mammography (DM), magnetic resonance imaging (MRI), and nuclear medicine procedures are commonly used. The gold standard for confirming cancer is histopathology. To effectively support the segmentation, diagnosis, and prognosis of BC. Artificial intelligence (AI) technologies show great promise for the quantitative depiction of medical images.This review explores recent strides in AI applications for BC. The literature search from 2018 to 2025 was performed with the PubMed database. It includes rapid breast lesion detection, segmentation, cancer diagnosis and enhanced imaging quality through data augmentation. It also discusses the biological characterization of BC via AI-based classification tools, including subtyping and staging. Furthermore, this review also explores the use of multiomics data to predict clinical outcomes such as survival, treatment response, and metastasis in BC. Additionally, we recognized the challenges faced by AI in BC in real-world applications, including organizing data, model interpretability, and regulatory compliance.