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Volume 45 Issue 5
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Article Navigation >  Journal of Molecular Imaging  > 2022  >  45(5): 779-789
YU Jingchun, GUO Mingxing, HAN Jing, ZHANG Xiaoying, CHEN Hanwei, WANG Hao. Progress of artificial intelligence in the field of pathological diagnosis[J]. Journal of Molecular Imaging, 2022, 45(5): 779-789. doi: 10.12122/j.issn.1674-4500.2022.05.29
Citation: YU Jingchun, GUO Mingxing, HAN Jing, ZHANG Xiaoying, CHEN Hanwei, WANG Hao. Progress of artificial intelligence in the field of pathological diagnosis[J]. Journal of Molecular Imaging, 2022, 45(5): 779-789. doi: 10.12122/j.issn.1674-4500.2022.05.29
shu

Progress of artificial intelligence in the field of pathological diagnosis

doi: 10.12122/j.issn.1674-4500.2022.05.29
  • 1.

    South China Normal University-Panyu Central Hospital Joint Laboratory of Basis and Translational Medical Research, Guangzhou 511400, China

  • 2.

    College of Life Sciences, South China Normal University, Guangzhou 510630, China

  • 3.

    Department of Pathology, Guangzhou Panyu District Central Hospital, Guangzhou 511400, China

  • Received Date: 2022-06-22
  • Publish Date: 2022-09-20
    Abstract
  • Based on the development of machine vision in recent years, the application of deep learning artificial intelligence methods in histopathology has greatly promoted pathologists to solve clinical diagnostic problems. This method can be called computer pathology. Artificial intelligence can help pathologists sift through most benign data, aid in diagnosis, predict efficacy, identify biomarkers, and even monitor therapeutic efficacy and identify unknown signals for drugs. Based on the in-depth study of deep learning in the field of pathology, it is possible for the computer to process pathological data automatically. Artificial intelligence diagnostic decisions are based on big data, and it is possible to personalize the management of each patient. It has the advantage of more rapid and accurate diagnosis for most common diseases. However, it is worth considering that the development of digital pathology is still limited by some problems, so that it is not widely used in digital pathology diagnostic platform at the present stage. We summarized the recent progress of artificial intelligence in the field of pathological diagnosis in this paper. We discussed the feasibility of this technology, added the difficulties and challenges encountered in digital pathology, and put forward the prospect of its practicality in this field.

     

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