Ensuring diagnostic performance of artificial intelligence (AI) before introduction into clinical practice is essential. Growing numbers of studies using AI for digital pathology have been reported over recent years. The aim of this work is to examine the diagnostic accuracy of AI in digital pathology images for any disease. This systematic review and meta-analysis included diagnostic accuracy studies using any type of AI applied to whole slide images (WSIs) for any disease. The reference standard was diagnosis by histopathological assessment and/or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model, with additional subgroup analyses also performed. Of 2976 identified studies, 100 were included in the review and 48 in the meta-analysis. Studies were from a range of countries, including over 152,000 whole slide images (WSIs), representing many diseases. These studies reported a mean sensitivity of 96.3% (CI 94.1–97.7) and mean specificity of 93.3% (CI 90.5–95.4). There was heterogeneity in study design and 99% of studies identified for inclusion had at least one area at high or unclear risk of bias or applicability concerns. Details on selection of cases, division of model development and validation data and raw performance data were frequently ambiguous or missing. AI is reported as having high diagnostic accuracy in the reported areas but requires more rigorous evaluation of its performance.

在引入临床实践之前确保人工智能 (AI) 的诊断性能至关重要。近年来，越来越多的研究使用人工智能进行数字病理学。这项工作的目的是检查人工智能在数字病理图像中对任何疾病的诊断准确性。这项系统回顾和荟萃分析包括使用任何类型的人工智能应用于任何疾病的整个幻灯片图像（WSI）的诊断准确性研究。参考标准是通过组织病理学评估和/或免疫组织化学进行诊断。检索于 2022 年 6 月在 PubMed、EMBASE 和 CENTRAL 中进行。使用 QUADAS-2 工具评估偏倚风险和适用性问题。数据提取由两名研究人员进行，并使用双变量随机效应模型进行荟萃分析，还进行了额外的亚组分析。在 2976 项已确定的研究中，100 项纳入综述，48 项纳入荟萃分析。研究来自多个国家，包括超过 152,000 张完整幻灯片图像 (WSI)，代表多种疾病。这些研究报告的平均敏感性为 96.3% (CI 94.1–97.7)，平均特异性为 93.3% (CI 90.5–95.4)。研究设计存在异质性，99% 确定纳入的研究至少有一个领域存在较高或不明确的偏倚风险或适用性问题。有关案例选择、模型开发和验证数据划分以及原始性能数据的详细信息经常含糊不清或缺失。据报道，人工智能在所报告的领域具有很高的诊断准确性，但需要对其性能进行更严格的评估。