Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

Figure 1 The convolutional neural network model. A convolutional neural network consists of an input layer, a few hidden layers and an output layer. It is commonly applied in medical imaging through the detection, segmentation and classification of image patterns.

Figure 2 Current status and future research direction for implementation of artificial intelligence-assisted endoscopy in clinical practice. In an upcoming era of artificial intelligence-assisted diagnosis, endoscopists can look forward to the continued development of new artificial intelligence systems for varying purposes. From determining multicancer via biopsy to real-time endoscopies, artificial intelligence has the potential of assisting physicians to improve their diagnostic accuracies.