Hassell Lewis A, Forsythe Marika L, Bhalodia Ami, Lan Thanh, Rashid Tasnuva, Powers Astin, Bui Marilyn M, Brickman Arlen, Gu Qiangqiang, Bychkov Andrey, Cree Ian, Pantanowitz Liron
Department of Pathology, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma.
Department of Pathology, University of Chicago Medical Center, Chicago, Illinois.
Mod Pathol. 2025 Jul;38(7):100765. doi: 10.1016/j.modpat.2025.100765. Epub 2025 Apr 8.
The introduction of new diagnostic information in pathology requires effective dissemination and adoption strategies. Although 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. Although AI can improve efficiency and accuracy, it is 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.
病理学中引入新的诊断信息需要有效的传播和采用策略。尽管诸如期刊、会议和图谱等传统方法一直在使用，但它们在可及性、交互性和性能验证方面存在挑战。数字病理学（DP）以及人工智能或增强智能（AI）为解决这些局限性提供了有前景的解决方案。本文主张使用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、数字玻片库以及AI增强诊断工具的重要性。尽管AI可以提高效率和准确性，但解决诸如过度依赖AI、偏差以及需要人工监督等潜在问题至关重要。通过利用DP和AI，可以提高诊断、分级、分期和分类的效率与准确性，最终改善患者护理。
