The Artificial Intelligence and Nanotoxicology

Over the past two decades, there has been notable advancement in the development of methodologies and understanding pertaining to the safety of nanomaterials. However, it is imperative to maintain ongoing investment and dedication towards the responsible research and innovation of commercially applicable nanomaterials. These advancements result in the generation of extensive and intricate data sets, which are perfectly suited for modeling and analysis using state-of-the-art machine learning techniques. The full realization of the advantages of automation in nanomaterials has not yet been achieved, resulting in a delay in the implementation of machine learning techniques for data analysis. Here, we will discuss some important advancements and challenges in the utilization of machine learning techniques for the purpose of modeling and predicting potential negative impacts on biological and environmental systems caused by nanomaterials. It presents instances of how traditional machine learning and deep learning techniques are employed in the field of nanosafety. Additionally, it offers a broader understanding and future outlook on advancements that are expected or necessary in the near future to enable artificial intelligence to play a more significant role in ensuring nanosafety.

Keywords:

Artifcial Intelligence , Nanomedicine , Nanotoxicology , Machine Learning.