Development of Machine Learning and Artificial Intelligence in Toxic Pathology

Tianbo Song; Quan Zhang; Guoqing Cai; Meiqing Cai; Jili Qian

doi:10.54097/Be1ExjZa

Authors

Tianbo Song
Quan Zhang
Guoqing Cai
Meiqing Cai
Jili Qian

DOI:

https://doi.org/10.54097/Be1ExjZa

Keywords:

Toxicity Pathology, Artificial Intelligence, Machine Learning, Artificial Neural Network

Abstract

Toxicity pathology is an important part of preclinical drug safety evaluation. With the development of computer science and full-slice digital scanning technology, artificial intelligence (AI) has been widely used in the field of drug safety evaluation, including all aspects of pathology, such as diagnostic pathology, veterinary diagnostics, pathology research, regulatory toxicology and pathology primary film review and peer review. Toxicology is one of the most valuable disciplines to promote the development of animal and human health, and the toxicity research of drug non-clinical safety evaluation. The development and application of a wide variety of algorithms for histopathology suggests that AI pathology platforms can profoundly influence the future of digital toxic pathology, precision medicine, and personalized medicine. However, as with all other revolutionary technologies, there are many challenges in the implementation and application of AI pathology platforms. This paper reviews the development of artificial intelligence and machine learning, the application of artificial intelligence in toxic pathology, the application of machine learning in digital toxic pathology, and the impact of artificial intelligence on digital toxic pathology, in order to provide some reference for the application of artificial intelligence and machine learning in toxic pathology in China

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