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病理学人工智能应用在临床前和临床实施中的协同作用与挑战

Synergies and Challenges in the Preclinical and Clinical Implementation of Pathology Artificial Intelligence Applications.

作者信息

Qureshi Hammad A, Chetty Runjan, Kuklyte Jogile, Ratcliff Karl, Morrissey Maria, Lyons Caitriona, Rafferty Mairin

机构信息

Deciphex, DCU Alpha, Glasnevin, Dublin, Ireland.

出版信息

Mayo Clin Proc Digit Health. 2023 Nov 15;1(4):601-613. doi: 10.1016/j.mcpdig.2023.08.007. eCollection 2023 Dec.

Abstract

Recent introduction of digitalization in pathology has disrupted the field greatly with the potential to change the area immensely. Digital pathology has created the potential of applying advanced quantitative analysis and artificial intelligence (AI) to the domain. In this study, we present an overview of what pathology AI applications have the greatest potential of widespread adoption in the preclinical domain and subsequently, in the clinical setting. We also discuss the major challenges in AI adoption being faced by the field of digital and computational pathology. We review the research literature in the domain and present a detailed analysis of the most promising areas of digital and computational pathology AI research and identify applications that are likely to see the first adoptions of AI technology. Our analysis shows that certain areas and fields of application have received more attention and can potentially affect the field of digital and computational pathology more favorably, leading to the advancement of the field. We also present the main challenges that are faced by the field and provide a comparative analysis of various aspects that are likely to influence the field for the long term in the future.

摘要

病理学领域近期引入的数字化极大地扰乱了该领域,同时也具有极大改变该领域的潜力。数字病理学开创了将先进的定量分析和人工智能(AI)应用于该领域的可能性。在本研究中,我们概述了哪些病理学AI应用在临床前领域以及随后在临床环境中具有最广泛采用的最大潜力。我们还讨论了数字和计算病理学领域在采用AI时面临的主要挑战。我们回顾了该领域的研究文献,并对数字和计算病理学AI研究最有前景的领域进行了详细分析,确定了可能最早采用AI技术的应用。我们的分析表明,某些应用领域受到了更多关注,并且可能更有利地影响数字和计算病理学领域,从而推动该领域的发展。我们还介绍了该领域面临的主要挑战,并对可能在未来长期影响该领域的各个方面进行了比较分析。

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本文引用的文献

1
Built to Last? Reproducibility and Reusability of Deep Learning Algorithms in Computational Pathology.
Mod Pathol. 2024 Jan;37(1):100350. doi: 10.1016/j.modpat.2023.100350. Epub 2023 Oct 10.
2
Generalization of Deep Learning in Digital Pathology: Experience in Breast Cancer Metastasis Detection.
Cancers (Basel). 2022 Nov 3;14(21):5424. doi: 10.3390/cancers14215424.
3
RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval.
Med Image Anal. 2023 Jan;83:102645. doi: 10.1016/j.media.2022.102645. Epub 2022 Oct 1.
4
The future of artificial intelligence in digital pathology - results of a survey across stakeholder groups.
Histopathology. 2022 Jun;80(7):1121-1127. doi: 10.1111/his.14659. Epub 2022 May 11.
5
Automated quality assessment of large digitised histology cohorts by artificial intelligence.
Sci Rep. 2022 Mar 23;12(1):5002. doi: 10.1038/s41598-022-08351-5.
6
Faster than light (microscopy): superiority of digital pathology over microscopy for assessment of immunohistochemistry.
J Clin Pathol. 2023 May;76(5):333-338. doi: 10.1136/jclinpath-2021-207961. Epub 2022 Jan 17.
8
Integrating digital pathology into clinical practice.
Mod Pathol. 2022 Feb;35(2):152-164. doi: 10.1038/s41379-021-00929-0. Epub 2021 Oct 1.
9
Ethics of AI in Pathology: Current Paradigms and Emerging Issues.
Am J Pathol. 2021 Oct;191(10):1673-1683. doi: 10.1016/j.ajpath.2021.06.011. Epub 2021 Jul 10.

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