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Review Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 2025 Apr 7:100765. doi: 10.1016/j.modpat.2025.100765 Q17.12024

Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging and Classification

关于数字病理学和增强智能对诊断、分级、分期和分类问题的影响的优化思考 翻译改进

Lewis A Hassell  1, Marika L Forsythe  2, Ami Bhalodia  3, Thanh Lan  4, Tasnuva Rashid  5, Astin Powers  6, Marilyn M Bui  6, Arlen Brickman  7, Qiangqiang Gu  8, Andrey Bychkov  9, Ian Cree  10, Liron Pantanowitz  11

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作者单位

  • 1 University of Oklahoma Health Sciences, 940 Stanton L. Young Blvd, BMSB 451, Oklahoma City, OK 73104. Electronic address: lewis-hassell@ouhsc.edu.
  • 2 University of Chicago Medical Center.
  • 3 The Pathology Laboratory, Lake Charles, LA.
  • 4 Evanston Hospital, Evanston, IL.
  • 5 University of Florida, College of Medicine, Jacksonville.
  • 6 H. Lee Moffitt Cancer Center & Research Institute.
  • 7 Brown University, Warren Alpert School of Medicine.
  • 8 Mayo Clinic.
  • 9 Kameda Medical Center, Kamogawa, Chiba, Japan.
  • 10 International Agency for Research on Cancer, Lyon, France.
  • 11 University of Pittsburgh Medical Center, Pittsburgh.
  • DOI: 10.1016/j.modpat.2025.100765 PMID: 40204094

    摘要 中英对照阅读

    The introduction of new diagnostic information in pathology requires effective dissemination and adoption strategies. While traditional methods like journals, meetings, and atlases have been used, they pose challenges in accessibility, interactivity, and performance validation. Digital pathology (DP) and artificial or augmented intelligence (AI) offer promising solutions to address these limitations. This paper advocates the use of DP and AI tools to facilitate the introduction of new diagnostic information in pathology. It highlights the importance of standardized training and validation sets, digital slide libraries, and AI-enhanced diagnostic tools. While AI can improve efficiency and accuracy, it's crucial to address potential pitfalls such as over-reliance on AI, bias and the need for human oversight. By leveraging DP and AI, the efficiency and accuracy of diagnosis, grading, staging, and classification can be augmented, ultimately improving patient care.

    Keywords:digital pathology; augmented intelligence

    病理学中新的诊断信息的引入需要有效的传播和采用策略。虽然传统的期刊、会议和图谱等方法已被使用,但它们在可访问性、互动性和性能验证方面存在挑战。数字病理学(DP)和人工智能或增强智能(AI)提供了有前景的解决方案来解决这些限制。本文倡导使用DP和AI工具来促进病理学中新诊断信息的引入。它强调了标准化培训和验证集、数字切片库以及AI增强型诊断工具的重要性。虽然AI可以提高效率和准确性,但必须解决诸如对AI过度依赖、偏见及人类监督需求等潜在问题。通过利用DP和AI,诊断、分级、分期和分类的效率与准确性可以得到提升,最终改善患者护理。

    Copyright © 2025. Published by Elsevier Inc.

    关键词:数字病理学; 增强智能; 诊断分级分期分类

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    Copyright © Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc. 中文内容为AI机器翻译,仅供参考!

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    期刊名:Modern pathology

    缩写:MODERN PATHOL

    ISSN:0893-3952

    e-ISSN:1530-0285

    IF/分区:7.1/Q1

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    Toward Optimizing the Impact of Digital Pathology and Augmented Intelligence on Issues of Diagnosis, Grading, Staging and Classification