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Research progress in application of artificial intelligence and machine learning in toxicologic pathology
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Yi-hao LI1, Yi-yang TENG2, 3, Ya-qun ZHANG1, Zhuang QIAN2, Wen-yuan HU1, Xiao-qun ZHONG1, Jing HU1, Xiao-jun CHEN1, Zhen-long YAN1, Rui-nan PENG1, Ya WANG1, Hui LI1, Jian-ya GE1, Cheng-xian MIAO1, Wei SHAO1, Jian-jun LV1, Toko OHIRA2
Chinese Journal of New Drugs | 2023, 32(6) : 598 - 604
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Chinese Journal of New Drugs | 2023, 32(6): 598-604
Research progress in application of artificial intelligence and machine learning in toxicologic pathology
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Yi-hao LI1, Yi-yang TENG2, 3, Ya-qun ZHANG1, Zhuang QIAN2, Wen-yuan HU1, Xiao-qun ZHONG1, Jing HU1, Xiao-jun CHEN1, Zhen-long YAN1, Rui-nan PENG1, Ya WANG1, Hui LI1, Jian-ya GE1, Cheng-xian MIAO1, Wei SHAO1, Jian-jun LV1, Toko OHIRA2
Affiliations
  • 1InnoStar Bio-Tech Nantong Co., Ltd., Nantong 226133, China
  • 2Shanghai InnoStar Bio-Tech Co., Ltd., Shanghai 200043, China
  • 3China State Institute of Pharmaceutical Industry, Shanghai 201203, China
Published: 2023-03-30
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Toxicologic pathology is one of the most valuable disciplines contributing to the advancement of animal and human health. The gold standard of the toxicologic pathology evaluation in toxicity studies during nonclinical safety evaluation of drugs is considered to be the histopathological examination of paraffin-embedded, hematoxylin and eosin-stained tissue sections. Digital toxicologic pathology, artificial intelligence (AI), and in particular machine learning (ML) are globally disruptive, rapidly growing sectors of technology whose impact on the field of histopathology is quickly being realized. The development and application of increasing numbers of algorithms in the histopathological field have demonstrated that AI pathology platforms are now poised to truly impact the future of digital toxicologic pathology, precision medicine, and personalized medicine. However, as with all great technological advances, there are implementation and adoption challenges. The development of AI and ML, application of AI in toxicologic pathology, application of ML in digital toxicologic pathology, and impact of AI on digital toxicologic pathology were reviewed in the paper, in order to provide some references for applying AI and ML in toxicologic pathology in China.

toxicologic pathology  /  artificial intelligence  /  machine learning  /  deep learning  /  artificial neural network
Yi-hao LI, Yi-yang TENG, Ya-qun ZHANG, Zhuang QIAN, Wen-yuan HU, Xiao-qun ZHONG, Jing HU, Xiao-jun CHEN, Zhen-long YAN, Rui-nan PENG, Ya WANG, Hui LI, Jian-ya GE, Cheng-xian MIAO, Wei SHAO, Jian-jun LV, Toko OHIRA. Research progress in application of artificial intelligence and machine learning in toxicologic pathology[J]. Chinese Journal of New Drugs, 2023 , 32 (6) : 598 -604 .
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  • Online Date：2026-03-06
  • Published：2023-03-30
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  • Accepted：2022-07-04
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Affiliations
    1InnoStar Bio-Tech Nantong Co., Ltd., Nantong 226133, China
    2Shanghai InnoStar Bio-Tech Co., Ltd., Shanghai 200043, China
    3China State Institute of Pharmaceutical Industry, Shanghai 201203, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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