Kim Yoo Jung, Roh Eul Hee, Park Sangchan
Department of Health Services Management, Graduate School, Kyung Hee University, Seoul, Korea.
Department of Business Administration, College of Management, Kyung Hee University, Seoul, Korea.
J Exerc Rehabil. 2021 Feb 23;17(1):11-14. doi: 10.12965/jer.2142018.009. eCollection 2021 Feb.
Digital pathology incorporates the acquisition, management, sharing, and interpretation of pathological information, including slides and data, in a digital environment. Digital slides are created using a scanning device to capture a high-resolution image on glass slides for analysis on a computer or a mobile device. Though digital pathology has drastically grown over the last 10 years and has created opportunities to support specialists, few have attempted to address its full-scale implementation in routine clinical practice. To incorporate new technologies in diagnostic processes, it is necessary to study their application, the value they provide to specialists, and their effects on improvements across the entire workflow, rather than studying a particular element. In this study, we aimed to identify what have the current digital pathology systems contributed to the pathological and diagnostic process. We retrieved articles published between 2010 and 2020 from the databases PubMed and Google Scholar. We explored how digital pathology systems can better utilize existing medical data and new technologies within the current diagnostic workflow. While the evidence concerning the efficacy and effectiveness of digital pathology is mounting, high-quality evidence regarding its impact on resource allocation and value for diagnosis is still needed to support clinical diagnosis and policy decision-making.
数字病理学在数字环境中整合了病理信息（包括玻片和数据）的采集、管理、共享及解读。数字玻片通过扫描设备创建，用于在载玻片上捕获高分辨率图像，以便在计算机或移动设备上进行分析。尽管数字病理学在过去10年中取得了巨大发展，并为支持专家提供了机会，但很少有人尝试在常规临床实践中全面实施它。为了将新技术纳入诊断过程，有必要研究它们的应用、为专家提供的价值以及对整个工作流程改进的影响，而不是研究某个特定元素。在本研究中，我们旨在确定当前的数字病理学系统对病理和诊断过程有哪些贡献。我们从PubMed和谷歌学术数据库中检索了2010年至2020年间发表的文章。我们探讨了数字病理学系统如何能在当前诊断工作流程中更好地利用现有医学数据和新技术。虽然关于数字病理学有效性的证据越来越多，但仍需要高质量的证据来证明其对资源分配和诊断价值的影响，以支持临床诊断和政策决策。
