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The integration of artificial intelligence (AI) into cancer nanomedicine is transforming personalized therapy and diagnostics. Focusing on AI-guided design and optimization of nanomedicines, we evaluate how computational technologies can be used to improve targeting precision and therapeutic efficacy by matching treatments to each patient’s genetic and phenotypic profile. Surveying contemporary research, this Perspective presents a multidimensional view of AI-enabled nanomedicine that captures its technological, biological, and clinical complexity. Probabilistic and mechanistic models now facilitate the real-time adaptation of nanoparticle formulations. Looking ahead, we anticipate that AI will accelerate discovery and help nanomedicine realize its full potential in precision oncology. In addition to reviewing recent advances, we propose concrete guidelines for embedding AI throughout the nanomedicine pipeline, spanning data curation, model development, preclinical validation, and clinical translation. Finally, we highlight persistent gaps in standardized datasets, model interpretability, and regulatory alignment that must be addressed to achieve widespread clinical impact. Artificial intelligence is set to transform precision oncology by addressing challenges in targeting, efficacy, and therapeutic personalization. Its integration into cancer nanomedicine accelerates clinical translation by unifying technological, biological, and clinical complexities, enabling the design of nanoparticle therapies tailored to individual patient profiles.
Guan et al. (Mon,) studied this question.