Artificial Intelligence-Powered Translational Nanomedicine in Oncology: Clinical Developments, Multistage Targeting Techniques, And Prospects for Brain, Liver, Breast, and Kidney Cancers

Abstract

The emergence of artificial intelligence (AI) has proved to be an important development in the field of translational nanomedicine, as it has made possible intelligent and personalized applications for precision oncology. The integration of machine learning (ML), deep learning, nanoinformatics, predictive modeling, and computational methods with nano delivery systems has improved carrier design, formulation optimization, multiscale targeting, and therapy monitoring using AI. This article discusses some of the latest developments in the area of AI-assisted translational nanomedicine. It also highlights the use of AI in tumor identification, patient stratification, theranostics, and the application of AI in brain, liver, breast, and kidney cancers. Moreover, cutting-edge developments in digital twins, federated learning, explainable AI, large language models, and intelligent nanorobotics are described for their capacity to speed up clinical translation. While much has been accomplished, there are issues regarding data standardization, clinical validation, scalable manufacturing, regulatory clearance, and algorithm transparency that need to be addressed.