Oesophageal, stomach, colon, and rectal cancers are all types of gastrointestinal malignancies. Gastrointestinal malignancies are responsible for around 20% of all cancer diagnoses and 22.5% of cancer deaths worldwide. Nearly 3.5 million new cases of gastrointestinal cancer have been reported worldwide, according to the WHO. Gastric cancer is one of the most common causes of cancer-related mortality worldwide. Due to the inconspicuous and non-specific symptoms and indicators of stomach cancer, the majority of cases are discovered only in advanced stages, with a poor prognosis. Early detection, on the other hand, can result in a 5 year survival rate of more than 90%. The use of AI in medicine has gotten a strong interest in the last decade. Endoscopic diagnosis with AI assistance is a prominent topic in the study. AI refers to a computer’s ability to perform a task often performed by intelligent persons, such as the "learn" feature, which mimics human cognition. Two AI subfields are machine learning and deep learning. In medical computer vision, artificial intelligence based diagnostic support systems, particularly Convolutional Neural Network (CNN) based image processing tools, have shown considerable promise. XAI (Explainable Artificial Intelligence), a new discipline that meets this need and offers numerous approaches for providing some level of explanation to deep learning AI systems, solves this need. This systematic review summarizes recent studies in gastric cancer and CNN based approaches for characterization and prognostication of gastrointestinal cancer pathology, as well as potential limits and future possibilities for AI in gastric cancer.
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