For the millions of women diagnosed with breast cancer annually, the gap between a timely, accurate diagnosis and a missed or delayed one can be decisive. The central limitation of current clinical practice — relying on a single imaging modality or a standalone pathology report — may be approaching obsolescence as deep learning architectures learn to synthesize radically different data streams into a unified diagnostic picture.
This review maps the state of AI-driven multimodal fusion in breast cancer, cataloguing how models now integrate radiomics extracted from MRI, mammography, and ultrasound with digital pathology slide analysis, multi-omics signatures (genomic, transcriptomic, proteomic), circulating tumor DNA from liquid biopsies, and structured clinical variables. The fusion frameworks examined — including attention-based transformers and graph neural networks — do not simply stack these inputs; they mine cross-modal associations invisible to any single modality alone. Reported performance gains span molecular subtype classification, treatment response prediction, and prognostic staging. The review also covers emerging architectures: digital twin modeling that simulates individual tumor trajectories, minimal residual disease monitoring via dynamic liquid biopsy, and multimodal large language models capable of synthesizing heterogeneous clinical data into actionable outputs.
Several important caveats temper enthusiasm. Virtually all reviewed models were developed on single-institution datasets, creating significant uncertainty about generalizability across scanner vendors, staining protocols, and patient populations. Model interpretability remains a persistent clinical barrier — a black-box system that outperforms a radiologist in controlled conditions offers limited utility if oncologists cannot interrogate its reasoning. Data standardization across modalities and institutions is unsolved infrastructure, not an algorithmic problem. For health-conscious adults, the practical horizon is a five-to-ten year timeline before multimodal AI tools are routinely embedded in clinical workflows. This is a confirmatory and synthesizing review rather than a trial — its value lies in mapping convergence across previously siloed research streams, signaling that integration, not incremental improvement of individual tools, is the field's productive frontier.