Translational gaps and clinical readiness of artificial intelligence and multimodal imaging in breast cancer...

A persistent translational gap separates the high research-benchmark performance of artificial intelligence (AI) and advanced imaging in breast cancer from demonstrated real-world clinical utility. Most systems reporting accuracy above 95% on curated datasets have not been externally validated across diverse populations and imaging platforms.

We critically examine AI-driven diagnostics and advanced imaging modalities for breast cancer, focusing on the barriers-generalization failure, algorithmic bias, reproducibility, and workflow integration-that determine clinical readiness rather than on benchmark capability alone.

We conducted a structured review of peer-reviewed literature (January 2015-March 2025) across PubMed, Scopus, IEEE Xplore, and Web of Science, combining breast cancer terms with AI, deep learning, vision transformers, digital breast tomosynthesis (DBT), contrast-enhanced mammography (CEM), MRI, and liquid biopsy. Study selection followed an explicit, PRISMA-style screening workflow; 127 of 312 identified records met predefined eligibility and quality criteria.

Reported benchmark performance is consistently optimistic relative to clinical performance: deep learning exceeds 95% accuracy and vision transformers report near-perfect classification on CBIS-DDSM, yet a reproducible 5-15% degradation follows deployment owing to distribution shift, scanner and vendor heterogeneity, and population differences. Among validated technologies, DBT raises cancer detection by 27% while lowering false positives; CEM increases sensitivity from 71.5% to 92.7% in dense breasts; and the Mirai risk model (AUC 0.76-0.81) outperforms traditional clinical tools. Liquid biopsy offers high-specificity molecular profiling but inadequate sensitivity (33%) for population screening. Critically, technical maturity is decoupled from validation depth and equity readiness-the dimensions that actually gate adoption.

Realizing the potential of AI-augmented, risk-stratified screening requires multicenter prospective and multi-vendor external validation, fairness-aware design with subgroup reporting, transparent reproducibility, and adaptive regulation of continuously learning systems. We propose an operationally defined translational readiness framework that scores each technology along six dimensions, locating it on the continuum from research capability to equitable clinical deployment.