Artificial intelligence and machine learning disciplines with the potential to improve the nanotoxicology and nanomedicine fields: a comprehensive review

The use of nanomaterials in medicine depends largely on nanotoxicological evaluation in order to ensure safe application on living organisms. Artificial intelligence (AI) and machine learning (MI) can be used to analyze and interpret large amounts of data in the field of toxicology, such as data from toxicological databases and high-content image-based screening data. Physiologically based pharmacokinetic (PBPK) models and nano-quantitative structure-activity relationship (QSAR) models can be used to predict the behavior and toxic effects of nanomaterials, respectively. PBPK and Nano-QSAR are prominent ML tool for harmful event analysis that is used to understand the mechanisms by which chemical compounds can cause toxic effects, while toxicogenomics is the study of the genetic basis of toxic responses in living organisms. Despite the potential of these methods, there are still many challenges and uncertainties that need to be addressed in the field. In this review, we provide an overview of artificial intelligence (AI) and machine learning (ML) techniques in nanomedicine and nanotoxicology to better understand the potential toxic effects of these materials at the nanoscale.

Automated summary

The use of nanomaterials in medicine requires nanotoxicological evaluation to ensure safe application. Artificial intelligence and machine learning can analyze toxicological data and predict nanomaterial behavior and toxic effects. PBPK and Nano-QSAR models can analyze harmful events and toxicogenomics studies genetic basis of toxic responses. However, challenges and uncertainties still exist in this field. This review gives an overview of AI and ML techniques in nanomedicine and nanotoxicology to understand the potential toxic effects of nanomaterials at the nanoscale.