Integrating Machine Learning With Nanotechnology For Enhanced Cancer Detection And Treatment

Abstract

Cancer remains one of the leading causes of mortality worldwide, necessitating the development of more effective diagnostic and therapeutic methods. Advances in both nanotechnology and machine learning (ML) offer promising solutions to these challenges. Nanotechnology enables the manipulation of materials at the molecular or atomic scale, which facilitates precise drug delivery, early-stage cancer detection, and targeted therapies. Machine learning, with its ability to process vast amounts of data and recognize complex patterns, can significantly enhance the efficacy of nanotechnology-based interventions. This paper explores the integration of machine learning with nanotechnology, discussing its applications in cancer detection, diagnosis, and treatment. By analyzing current research, we highlight the synergies between these fields, the technical challenges, and the future potential for developing more personalized and efficient cancer therapies. Furthermore, we consider ethical and safety concerns, along with recommendations for future interdisciplinary research.