Artificial Intelligence-Driven Translational Nanomedicine in Oncology: Multistage Targeting Strategies, Clinical Advances, and Future Directions for Liver, Breast, Kidney, and Brain Cancers

Abstract

Artificial intelligence (AI) and translational nanomedicine are revolutionizing the field of precision oncology through the use of smart nanoparticles, personalization of drug delivery, and evidence-based therapeutic decisions. This review highlights some of the recent developments in AI-driven translational nanomedicine, with special emphasis on multistage targeting approaches and clinical utility in liver, breast, renal, and brain tumors. Machine learning, deep learning, radiomics, multimodal omics, and nanoinformatics are among the areas discussed in this review. Tumor heterogeneity, vascular barrier, immune microenvironment, renal clearance, and blood-brain barrier penetration in organs are considered in order to highlight the necessity for the development of personalized nanomedicine strategies. Despite the advancements that have been made by AI-guided nanomedicine in drug delivery, pharmacokinetics and safety, there are several obstacles that hinder the clinical application of such technology. The above review suggests future strategies which include standardization in nanoinformatics, patient-derived organoids, digital twins, federated learning, adaptive clinical trials based on biomarkers, and explainable AI for accelerating clinical translation. It may be concluded that the combination of AI and translational nanomedicine offers an attractive approach towards the development of safer and effective cancer treatment options.