An increasing number of artificial intelligence (AI) tools are moving towards the clinical realm in histopathology and across medicine. The introduction of such tools will bring several benefits to diagnostic specialities, namely increased diagnostic accuracy and efficiency, however, as no AI tool is infallible, their use will inevitably introduce novel errors. These errors made by AI tools are, most fundamentally, misclassifications made by a computational algorithm. Understanding of how these translate into clinical impact on patients is often lacking, meaning true reporting of AI tool safety is incomplete. In this Perspective we consider AI diagnostic tools in histopathology, which are predominantly assessed in terms of technical performance metrics such as sensitivity, specificity and area under the receiver operating characteristic curve. Although these metrics are essential and allow tool comparison, they alone give an incomplete picture of how an AI tool’s errors could impact a patient’s diagnosis, management and prognosis. We instead suggest assessing and reporting AI tool errors from a pathological and clinical stance, demonstrating how this is done in studies on human pathologist errors, and giving examples where available from pathology and radiology. Although this seems a significant task, we discuss ways to move towards this approach in terms of study design, guidelines and regulation. This Perspective seeks to initiate broader consideration of the assessment of AI tool errors in histopathology and across diagnostic specialities, in an attempt to keep patient safety at the forefront of AI tool development and facilitate safe clinical deployment.

越来越多的人工智能 (AI) 工具正在走向组织病理学和医学领域的临床领域。此类工具的引入将为诊断专业带来多种好处，即提高诊断准确性和效率，但是，由于没有任何人工智能工具是万无一失的，它们的使用将不可避免地引入新的错误。人工智能工具所犯的这些错误从根本上来说是计算算法造成的错误分类。通常缺乏对这些如何转化为对患者的临床影响的了解，这意味着人工智能工具安全性的真实报告是不完整的。在本视角中，我们考虑组织病理学中的人工智能诊断工具，这些工具主要根据技术性能指标进行评估，例如灵敏度、特异性和接受者操作特征曲线下的面积。尽管这些指标很重要并且可以进行工具比较，但它们本身并不能完整地说明人工智能工具的错误如何影响患者的诊断、管理和预后。相反，我们建议从病理和临床立场评估和报告人工智能工具错误，展示在人类病理学家错误的研究中如何做到这一点，并给出病理学和放射学中可用的示例。尽管这似乎是一项重要的任务，但我们讨论了在研究设计、指南和监管方面实现这一方法的方法。本视角旨在对组织病理学和跨诊断专业的人工智能工具错误评估进行更广泛的考虑，试图将患者安全置于人工智能工具开发的最前沿，并促进安全的临床部署。