[Purpose] The pathology is decisive for disease diagnosis, but relies heavily
on the experienced pathologists. Recently, pathological artificial intelligence
(PAI) is thought to improve diagnostic accuracy and efficiency. However, the
high performance of PAI based on deep learning in the laboratory generally
cannot be reproduced in the clinic. [Methods] Because the data preparation is
important for PAI, the paper has reviewed PAI-related studies in the PubMed
database published from January 2017 to February 2022, and 118 studies were
included. The in-depth analysis of methods for preparing data is performed,
including obtaining slides of pathological tissue, cleaning, screening, and
then digitizing. Expert review, image annotation, dataset division for model
training and validation are also discussed. We further discuss the reasons why
the high performance of PAI is not reproducible in the clinical practices and
show some effective ways to improve clinical performances of PAI. [Results] The
robustness of PAI depend on randomized collection of representative disease
slides, including rigorous quality control and screening, correction of digital
discrepancies, reasonable annotation, and the amount of data. The digital
pathology is fundamental of clinical-grade PAI, and the techniques of data
standardization and weakly supervised learning methods based on whole slide
image (WSI) are effective ways to overcome obstacles of performance
reproduction. [Conclusion] The representative data, the amount of labeling and
consistency from multi-centers is the key to performance reproduction. The
digital pathology for clinical diagnosis, data standardization and technique of
WSI-based weakly supervised learning hopefully build clinical-grade PAI.
Keywords: pathological artificial intelligence; data preparation;
clinical-grade; deep learning

该研究通过回顾 PubMed 数据库中 2017 年 1 月至 2022 年 2 月的研究，并进行 118 项研究的深入分析，总结出了数字病理学是临床级 PAI 的基础，数据标准化和基于全体切片图像的弱监督学习技术是克服临床效果不可复现的有效方式，其中代表性数据、标记数量和多中心一致性是性能重现的关键。