Artificial intelligence (AI) for the purpose of this review is an umbrella term for technologies emulating a nephropathologist’s ability to extract information on diagnosis, prognosis, and therapy responsiveness from native or transplant kidney biopsies. Although AI can be used to analyze a wide variety of biopsy-related data, this review focuses on whole slide images traditionally used in nephropathology. AI applications in nephropathology have recently become available through several advancing technologies, including (i) widespread introduction of glass slide scanners, (ii) data servers in pathology departments worldwide, and (iii) through greatly improved computer hardware to enable AI training. In this review, we explain how AI can enhance the reproducibility of nephropathology results for certain parameters in the context of precision medicine using advanced architectures, such as convolutional neural networks, that are currently the state of the art in machine learning software for this task. Because AI applications in nephropathology are still in their infancy, we show the power and potential of AI applications mostly in the example of oncopathology. Moreover, we discuss the technological obstacles as well as the current stakeholder and regulatory concerns about developing AI applications in nephropathology from the perspective of nephropathologists and the wider nephrology community. We expect the gradual introduction of these technologies into routine diagnostics and research for selective tasks, suggesting that this technology will enhance the performance of nephropathologists rather than making them redundant.

就本综述而言，人工智能 (AI) 是模拟肾病理学家从天然或移植肾活检中提取有关诊断、预后和治疗反应性信息的能力的技术的总称。尽管 AI 可用于分析各种活检相关数据，但本综述侧重于传统上用于肾病理学的整个幻灯片图像。肾病理学中的 AI 应用最近通过几种先进技术变得可用，包括 (i) 广泛引入载玻片扫描仪，(ii) 全球病理学部门的数据服务器，以及 (iii) 通过大大改进计算机硬件来实现 AI 培训。在本次审查中，我们解释了人工智能如何使用先进的架构（例如卷积神经网络）来提高精准医学背景下某些参数的肾病理学结果的可重复性，这些架构目前是用于该任务的机器学习软件的最新技术。由于肾病理学中的 AI 应用仍处于起步阶段，我们主要在肿瘤病理学示例中展示了 AI 应用的力量和潜力。此外，我们从肾病理学家和更广泛的肾病学界的角度讨论了在肾病理学中开发人工智能应用的技术障碍以及当前利益相关者和监管方面的担忧。我们期望将这些技术逐步引入常规诊断和选择性任务研究中，