Makhlouf Yasmine, Salto-Tellez Manuel, James Jacqueline, O'Reilly Paul, Maxwell Perry
Precision Medicine Centre of Excellence, PathLAKE Programme, The Patrick G Johnston Centre for Cancer Research, Queen's University Belfast, Belfast BT9 7AE, UK.
Division of Molecular Pathology, The Institute of Cancer Research, Sutton SM2 5NG, UK.
Diagnostics (Basel). 2022 May 20;12(5):1272. doi: 10.3390/diagnostics12051272.
Integrating artificial intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel and yet interconnected processes, namely the definition of the pathologist's task to be delivered , and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.
将人工智能(AI)工具整合到组织诊断工作流程中,将使病理学家乃至最终患者受益。此类AI工具的生成有两个并行且相互关联的过程,即确定要交付的病理学家任务以及软件开发要求。在这篇综述论文中,我们从经验丰富的病理学家和数据科学家相结合的视角,通过提出一个通用路径并描述构建AI数字病理工具的核心步骤,来揭开这一过程的神秘面纱。在此过程中,我们强调了AI科学家与病理学家之间合作的重要性,从假设的最初形成到最终可用产品。