Radiomics and Artificial Intelligence in Breast Cancer Imaging: Future Directions and Clinical Applicability

Breast cancer (BC) continues to be the most prevalent malignancy affecting women globally, representing a major public health concern, with significant morbidity and mortality. Early detection, accurate diagnosis, and precise characterisation of breast lesions are important to improve patient outcomes and survival rates. Conventional imaging modalities, such as mammography, ultrasound, and magnetic resonance imaging (MRI), have played pivotal roles in BC diagnosis but face limitations related to subjective interpretation, variability between radiologists, and challenges in detecting biologically aggressive subtypes. Radiomics and artificial intelligence (AI) have emerged as revolutionary adjuncts to enhance the diagnostic and prognostic capabilities of breast imaging. Radiomics involves the extraction of high-dimensional quantitative imaging features from standard medical images that are imperceptible to the human eye. These features can reveal tumour heterogeneity, microenvironment characteristics, and biological behaviour, thereby enriching information traditionally derived from visual inspection. AI, particularly through machine learning and deep learning models, enables automated analysis, pattern recognition, and prediction of clinical outcomes with high accuracy and reproducibility. The integration of radiomics and AI into BC imaging workflows holds the potential to shift the paradigm towards precision oncology, offering individualised risk stratification, early prediction of treatment response, and real-time decision support. However, this field faces significant challenges, including issues related to data standardisation, reproducibility, model validation, regulatory approval, and clinical integration. Ethical considerations regarding the data privacy, bias, and explainability of AI algorithms also remain critical hurdles. This comprehensive review delves into the fundamental concepts of radiomics and AI, summarises their current applications in BC imaging, and explores their evolving roles in clinical practice. It highlights recent advances, presents case studies demonstrating the clinical impact, and discusses ongoing research efforts aimed at overcoming the existing limitations. Furthermore, future directions, including the integration of radio genomics, explainable AI (XAI), and multi-omics approaches, were thoroughly examined to provide a roadmap for the clinical applicability of these technologies. As the convergence of advanced imaging analytics and computational intelligence continues to mature, radiomics and AI have been poised to redefine BC management, ushering in a new era of more accurate, efficient, and personalised patient care.