box What is already known about this subject? ► Computer-assisted systems for health image analysis have improved the medical decision-making process for diagnosing and analysing the progression of various diseases. ► Diseases affecting gastric tissue are a worldwide health problem. ► Deep learning applications presented good results in different domains, however its application on gastric tissue analysis is recent, poorly analysed, and standardised. What are the new findings? ► We provide a literature categorisation, based on the method and related tasks, identifying the most widely adopted deep learning architecture and data source used. ► This is the first systematic review dedicated to map gastric tissue deep learning applications covering a broad spectrum, also listing and evaluating open source tools. ► We identified gaps evaluation metrics, image collection availability and, consequently, implications for experimental reproducibility.How might it impact on clinical practice in the foreseeable future?► Deep learning applications can provide greater and more efficient workflow support and extraction of important information from histological images, consequently, replicable studies need to be conducted clearly, and transparently, also providing the data used.AbSTrACT background In recent years, deep learning has gained remarkable attention in medical image analysis due to its capacity to provide results comparable to specialists and, in some cases, surpass them. Despite the emergence of deep learning research on gastric tissues diseases, few intensive reviews are addressing this topic.Method We performed a systematic review related to applications of deep learning in gastric tissue disease analysis by digital histology, endoscopy and radiology images.Conclusions This review highlighted the high potential and shortcomings in deep learning research studies applied to gastric cancer, ulcer, gastritis and non-malignant diseases. Our results demonstrate the effectiveness of gastric tissue analysis by deep learning applications. Moreover, we also identified gaps of evaluation metrics, and image collection availability, therefore, impacting experimental reproducibility.