Published online Apr 14, 2019. doi: 10.3748/wjg.v25.i14.1666
Peer-review started: February 12, 2019
First decision: February 26, 2019
Revised: March 4, 2019
Accepted: March 16, 2019
Article in press: March 16, 2019
Published online: April 14, 2019
Processing time: 63 Days and 13.4 Hours
Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
Core tip: Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. The convolutional neural network exhibited outstanding performance in image analysis. AI has been applied in the field of gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent pitfalls of selection bias, overfitting, and spectrum bias (class imbalance) have the possibility of overestimating the accuracy and generalizing the result. Therefore, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. DL has its own lack of interpretability, and further investigations should be performed on this issue.