Traditionally, pathologists examine tissue slides under a microscope to find pathological lesions, and have the burden of finding the lesions among so many histopathology slides. Furthermore, inconsistency of diagnoses results differ corresponding to training among researchers. Therefore, accumulated research experience has led to the use of novel tools for increasing accuracy and consistency of diagnoses. With rapid transition from analog to digital methods and new developments in digital pathology, it is possible to use whole slide imaging (WSI) by scanning glass slides. Artificial intelligence (AI), including machine learning and deep learning using WSI, is starting to be applied to automatically classify and count microscope images, and this method has been expanded to include the field of medical image analysis. This review aims to define current trends toward AI application in the biomedical area, especially in the field of toxicopathology, outline current future business trends, and discuss multiple issues of diagnosis, quantification, three-dimensional reconstruction, molecular pathological research, and the future direction of AI in toxicopathology. Big data systems including a large amount of welldefined toxicopathological information will be highly useful for accuracy and corrections of diagnoses. In addition, the need for critical peer review is profound in the continuing educational process. Taken together, it is highly promising that AI model based on big data in the toxicopathological field could classify, detect, and segment pathological lesions in numerous organs of experimental animals and could help explain various biological mechanisms. This promising approach will provide an accurate and fast analysis of tissue structure and biological pathways using AI algorithms and big data.

Introduction
인공지능의 개념
전통적 독성병리와 인공지능 기술 도입 배경
독성병리 분야에 인공지능의 도입 효과
독성병리자료의 디지털화 필요성
독성병리 연구에서 인공지능 활용 분야
    병변의 진단
    정량화 연구
    3차원 이미지 구현
    분자병리학 연구
    조직진단 오류 수정 및 피어리뷰(peer review) 체계 구축
    교육시스템의 체계적인 구축
    독성병리 관련 빅데이터(big data) 구축
Conclusion
References

• Jin Seok Kang(Department of Biomedical Laboratory Science, Namseoul University) Corresponding author