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Most systems reporting accuracy above 95% on curated datasets have not been externally validated across diverse populations and imaging platforms.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>We critically examine AI-driven diagnostics and advanced imaging modalities for breast cancer, focusing on the barriers\u2014generalization failure, algorithmic bias, reproducibility, and workflow integration\u2014that determine clinical readiness rather than on benchmark capability alone.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a structured review of peer-reviewed literature (January 2015\u2013March 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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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\u201315% 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\u22120.81) 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\u2014the dimensions that actually gate adoption.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>\n                      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\n                      <jats:italic>operationally defined<\/jats:italic>\n                      translational readiness framework that scores each technology along six dimensions, locating it on the continuum from research capability to equitable clinical deployment.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s12672-026-05608-7","type":"journal-article","created":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T00:02:11Z","timestamp":1784419331000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Translational gaps and clinical readiness of artificial intelligence and multimodal imaging in breast cancer diagnostics"],"prefix":"10.1007","volume":"17","author":[{"given":"Anas","family":"Shahin","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohamad Faisal","family":"ALibrahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Ehsan","family":"Kawas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reham","family":"Elawi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,19]]},"reference":[{"key":"5608_CR1","unstructured":"World Health Organization: The global breast cancer initiative. 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