| dc.description.abstract | Breast cancer remains a leading cause of mortality among women worldwide. Early and
accurate diagnosis is critical to improving survival rates, yet conventional diagnostic
techniques, such as mammography, are often limited in integrating diverse clinical
data sources. This review explores the transformative potential of multimodal artificial
intelligence models, which combine Electronic Health Records (EHRs) and imaging
data to enhance diagnostic precision and treatment planning. We analyze advanced
architectures, including Convolutional Neural Networks (CNNs), transformers, and
fusion layers, evaluating their strengths, limitations, and clinical applicability. Key
challenges, such as data heterogeneity, computational demands, and the lack of
standardized datasets, are identified and discussed. This review also highlights the
gaps in current research, such as inconsistent evaluation criteria and suboptimal fusion
techniques, while proposing innovative solutions, including adaptive fusion methods
and lightweight architectures, to bridge these gaps. The findings emphasize the need
for standardized datasets and efficient multimodal models to foster broader adoption
in clinical settings. Future directions underscore the importance of developing scalable
and interpretable systems that can integrate seamlessly into oncology workflows, paving
the way for improved breast cancer diagnosis and personalized care. | |
