The number of publications in endoscopic journals that present deep learning applications has risen tremendously over the past years. Deep learning has shown great promise for automated detection, diagnosis and quality improvement in endoscopy. However, the interdisciplinary nature of these works has undoubtedly made it more difficult to estimate their value and applicability. In this review, the pitfalls and common misconducts when training and validating deep learning systems are discussed and some practical guidelines are proposed that should be taken into account when acquiring data and handling it to ensure an unbiased system that will generalize for application in routine clinical practice. Finally, some considerations are presented to ensure correct validation and comparison of AI systems.

在过去几年中，内窥镜期刊上介绍深度学习应用的出版物数量大幅增加。深度学习在内窥镜检查中显示出自动化检测、诊断和质量改进的巨大前景。然而，这些作品的跨学科性质无疑使得评估它们的价值和适用性变得更加困难。在这篇综述中，讨论了训练和验证深度学习系统时的陷阱和常见的不当行为，并提出了一些在获取数据和处理数据时应考虑的实用指南，以确保一个无偏见的系统可以推广到常规临床实践中的应用. 最后，提出了一些注意事项，以确保正确验证和比较 AI 系统。