Artificial Intelligence Integration With Nanotechnology for Cancer Therapy

Cancer nanomedicine offers a versatile platform for improving therapeutic index, but its clinical translation remains limited by unpredictable in vivo behavior, heterogeneous biological contexts, and inefficient design paradigms. Artificial intelligence (AI) is emerging as an integrative framework that links data-driven modeling with nanomedicine design, biological transport, and clinical decision-making. In this review, we discuss AI-guided strategies for material design, targeting, payload optimization, and in vivo delivery, with particular attention to protein corona-mediated biological identity, microenvironment-responsive activation, and biodistribution modeling. We further examine the translational requirements for AI-enabled nanomedicine, including data standardization, preclinical learning workflows, clinical stratification and risk-based governance. Finally, we outline future directions centered on transferable learning architectures, dynamic multiscale modeling and patient-aware therapeutic strategies. Together, these advances suggest that AI can move cancer nanomedicine beyond empirical formulation toward a more predictive, biologically informed, and clinically responsive discipline.