Translational gaps and clinical readiness of artificial intelligence and multimodal imaging in breast cancer diagnostics.
Artificial intelligence breast cancer diagnostics show 5-15% accuracy drop after real-world deployment
Translational gaps and clinical readiness of artificial intelligence and multimodal imaging in breast cancer diagnostics.
A persistent translational gap separates the high research-benchmark performance of artificial intelligence (artificial intelligence) and advanced imaging in breast cancer from demonstrated real-world clinical utility.
We critically examine artificial intelligence-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 artificial intelligence, deep learning, vision transformers, digital breast tomosynthesis (digital breast tomosynthesis), contrast-enhanced mammography (contrast-enhanced mammography), MRI, and liquid biopsy.
Among validated technologies, digital breast tomosynthesis raises cancer detection by 27% while lowering false positives; contrast-enhanced mammography 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 artificial intelligence-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.