Abstract

Personalized cancer therapy aims to tailor treatment strategies to the unique molecular, genetic, and phenotypic 
characteristics of individual patients. Nanomedicine has emerged as a powerful enabler of this paradigm by 
improving drug delivery, enhancing tumor selectivity, and reducing systemic toxicity. However, the rational 
design of effective nanomedicines is challenged by the complexity of cancer biology, interpatient heterogeneity, 
and the vast design space of nanomaterial properties. Artificial intelligence (AI) offers transformative tools to 
address these challenges by integrating large-scale biological, chemical, and clinical datasets to guide 
nanomedicine design and optimization. AI-driven approaches, including machine learning, deep learning, and 
reinforcement learning, can predict nanoparticle behavior, therapeutic efficacy, and patient-specific responses 
with unprecedented accuracy. This review examines the role of AI in guiding the design of nanomedicines for 
personalized cancer therapy. We discuss how AI enables data-driven nanoparticle engineering, patient 
stratification, and adaptive treatment strategies. Current applications, emerging platforms, and translational 
challenges are critically evaluated. By bridging nanotechnology and precision oncology, AI-guided 
nanomedicine holds significant promise for realizing truly personalized cancer treatment.