Artificial Intelligence-Enabled Nanomedicine in Precision Oncology: Diagnosis, Targeted Therapy, and Clinical Translation

Cancer nanomedicine offers programmable platforms for diagnosis, imaging, drug and gene delivery, and theranostics, but its clinical performance is constrained by tumor heterogeneity, complex nano-bio interactions, and interpatient variability. Artificial intelligence (AI) provides a means to integrate physicochemical, biological, imaging, molecular, and clinical data to improve the design and application of nanoscale cancer technologies. This Review examines the convergence of AI and nanotechnology across cancer diagnosis, therapeutic selection, treatment optimization, monitoring, drug development, and clinical translation, with particular emphasis on materials design and biological performance. AI-based approaches can support early detection, patient stratification, response and toxicity prediction, and rational therapeutic development. In parallel, nanoplatforms, including liposomes, polymeric nanoparticles, dendrimers, metallic and inorganic nanoparticles, biomimetic systems, and stimuli-responsive carriers, can improve pharmacokinetics, enhance tumor exposure, reduce systemic toxicity, and enable controlled delivery of diverse payloads. Their integration may advance precision nanomedicine by guiding nanoparticle composition, size, surface properties, targeting ligands, biodistribution, tumor accumulation, cellular uptake, protein-corona formation, release kinetics, toxicity, and therapeutic response. Conversely, nanodiagnostic and nanotheranostic systems can generate imaging, molecular, pharmacokinetic, and response data for AI-assisted patient selection and adaptive treatment monitoring. Translation will require standardized datasets, reproducible nanoparticle characterization, scalable manufacturing, explainable and externally validated models, regulatory clarity, long-term safety assessment, and equitable implementation. Together, AI-guided nanotechnology provides a framework linking materials design, biological interactions, and patient-specific cancer therapy.

Abstract from PubMed Central (PMID 42974481, PMC13726626). This entry was added automatically by our daily literature monitor because it matches the topics we follow; read the full paper at the original source.