Biomarker-driven and AI-assisted nanomedicine for breast cancer: advancing precision drug delivery from bench to bedside

INTRODUCTION: Breast cancer remains the most prevalent malignancy among women worldwide, with its pronounced molecular heterogeneity demanding therapeutic strategies that transcend conventional systemic chemotherapy toward tumor-selective, molecularly precise interventions. The convergence of biomarker science, engineered nanomedicine, and artificial intelligence (AI) offers a transformative framework for realizing this goal. AREAS COVERED: This review examines how molecular biomarkers (e.g. mucin glycoproteins, human epidermal growth factor receptor 2 (HER2), circulating tumor cells, and circulating tumor DNA) serve dual roles as diagnostic identifiers and active targeting ligands directing engineered nanocarriers. We critically evaluate biomarker-guided nanoplatforms (liposomal, polymeric, metallic, and protein-based architectures), stimuli-responsive drug release strategies, and AI-driven microfluidic manufacturing for scalable, good manufacturing practice-compliant nanoparticle production. Literature was retrieved from PubMed/MEDLINE, Scopus, and Web of Science (2000-2026), supplemented by ClinicalTrials.gov data. EXPERT OPINION: Near-term clinical impact is most realistically achieved through AI-assisted biomarker interpretation and multi-analyte liquid biopsy integration rather than novel nanoformulations, whose translational attrition remains high. Bridging the bench-to-bedside gap requires prospective biomarker-stratified clinical trials, co-developed companion diagnostics, and federated AI architectures as advances that will progressively replace static histological subtype classification with a continuously updated, molecularly individualized treatment paradigm for breast cancer.