Multimodal fusion and radiogenomic frameworks show promise for capturing heterogeneity in triple-negative breast cancerArtificial Intelligence Shows Promise in Triple-Negative Breast Cancer Care

BackgroundTriple-negative breast cancer (TNBC) is an aggressive and biologically heterogeneous breast cancer subtype for which robust biomarkers for diagnosis, treatment-response assessment, and prognosis remain limited. Artificial intelligence (AI) is increasingly used to analyze radiology, digital pathology, and molecular data in TNBC.MethodsThis Review provides a structured narrative synthesis of previously published studies on AI for TNBC, with emphasis on imaging, computational pathology, genomics, multi-omics, and multimodal fusion. The literature was organized by data modality, clinical task, validation strategy, and translational readiness, with particular attention to external validation, calibration, interpretability, and missing-data handling.ResultsAcross modalities, AI has been applied to lesion segmentation, subtype classification, prediction of pathological complete response after neoadjuvant therapy, recurrence-risk stratification, and survival modeling. Magnetic resonance imaging, ultrasound, mammography, whole-slide histopathology, transcriptomics, and multi-omics provide complementary information, while multimodal fusion and radiogenomic frameworks appear most promising for capturing TNBC heterogeneity. However, the current evidence base is still limited by small cohorts, inconsistent endpoint definitions, non-patient-level splitting, inadequate external testing, and domain shift across scanners, stains, assays, and institutions.DiscussionThe most clinically credible TNBC AI studies are those aligned with actionable clinical decisions and supported by robust validation, transparent reporting, and biologically grounded interpretation. Future progress will depend on multi-institutional data curation, self-supervised and foundation-model pretraining, privacy-preserving collaboration, and multimodal designs that remain reliable under missing modalities and real-world distribution shift.