Published online Jun 14, 2021. doi: 10.3748/wjg.v27.i22.2979
Peer-review started: February 3, 2021
First decision: February 27, 2021
Revised: March 10, 2021
Accepted: April 22, 2021
Article in press: April 22, 2021
Published online: June 14, 2021
The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.
Core Tip: Artificial intelligence-assisted endoscopy can assist physicians in the screening and diagnosis of gastric cancer. Most of the systems developed so far, applied in images and videos and using white light imaging and narrow-band imaging endoscopies, have achieved accuracies and sensitivities of at least 80%. However, the efficacy of artificial intelligence applications in gastric cancer depends on its intended role in clinical practice, and there have not been any attempts of clinical trials yet. This review summarizes the existing artificial intelligence applications in gastric cancer and pinpoints future research directions for their clinical practice implementation.