Authors: Manoj Sekhar
Abstract: Nanomedicine has revolutionized therapeutic strategies by enabling targeted drug delivery, controlled release, and improved bioavailability. However, the complexity of biological systems and variability among patients often limits the efficacy of nanomedicine therapies. Machine learning (ML), a subset of artificial intelligence, offers powerful tools for analyzing large datasets, predicting therapeutic outcomes, and optimizing nanomedicine design and administration protocols. This paper explores how machine learning techniques can enhance the efficacy of nanomedicine therapies by improving nanoparticle design, personalizing treatment regimens, predicting patient responses, and monitoring treatment progress in real time. It discusses recent advances, challenges, ethical considerations, and future prospects, emphasizing the critical role of ML in transforming nanomedicine from a one-size-fits-all approach to precision medicine.
DOI: http://doi.org/10.61137/ijsret.vol.8.issue6.553
