Cancer nanomedicine has evolved from the 1995 landmark approval of Doxil® into a programmable platform of precision oncology. The field now progresses along a coherent continuum that begins with passive enhanced permeability and retention (EPR)-mediated tumor accumulation, advances to active ligand–receptor targeting, and culminates in stimuli-responsive carriers whose cargo is liberated only when triggered by endogenous (acidic pH, redox imbalance, elevated GSH, dysregulated enzymes, ROS) or exogenous (light, magnetic, ultrasound, X-ray, electric) cues intrinsic to the tumor microenvironment (TME). This review maps this continuum, highlighting how the integration of patient-specific multi-omics data with artificial intelligence (AI) is converting tumor heterogeneity into quantitative design rules for nanocarrier optimization, validated in patient-derived organoids. Despite over 15 FDA-approved cancer nanomedicines and a robust clinical pipeline, translation is impeded by biological barriers, protein corona-mediated toxicity, manufacturing scalability issues, and a fragmented regulatory landscape. To bridge this bench-to-bedside chasm, we propose a convergent roadmap: safe-by-design engineering, quality-by-design modular manufacturing, and AI-guided digital twins coupled with micro/nano-robotic delivery for real-time, adaptive dosing. Realizing this vision will transform nanomedicine from an empirical carrier technology into a patient-calibrated, closed-loop therapeutic engine, cementing its role as the frontline of precision oncology.

A novel Cd-based MOF shows high sensitivity (K_sv ∼ 10⁶ M⁻¹) and selectivity (μM level) towards antibiotics was presented.