Evolving role of artificial intelligence in gastrointestinal endoscopy

World J Gastroenterol. 2020 Dec 14;26(46):7287-7298. doi: 10.3748/wjg.v26.i46.7287.


Artificial intelligence (AI) is a combination of different technologies that enable machines to sense, comprehend, and learn with human-like levels of intelligence. AI technology will eventually enhance human capability, provide machines genuine autonomy, and reduce errors, and increase productivity and efficiency. AI seems promising, and the field is full of invention, novel applications; however, the limitation of machine learning suggests a cautious optimism as the right strategy. AI is also becoming incorporated into medicine to improve patient care by speeding up processes and achieving greater accuracy for optimal patient care. AI using deep learning technology has been used to identify, differentiate catalog images in several medical fields including gastrointestinal endoscopy. The gastrointestinal endoscopy field involves endoscopic diagnoses and prognostication of various digestive diseases using image analysis with the help of various gastrointestinal endoscopic device systems. AI-based endoscopic systems can reliably detect and provide crucial information on gastrointestinal pathology based on their training and validation. These systems can make gastroenterology practice easier, faster, more reliable, and reduce inter-observer variability in the coming years. However, the thought that these systems will replace human decision making replace gastrointestinal endoscopists does not seem plausible in the near future. In this review, we discuss AI and associated various technological terminologies, evolving role in gastrointestinal endoscopy, and future possibilities.

Keywords: Artificial intelligence; Endoscopic diagnosis; Gastric cancer; Gastrointestinal diseases; Gastrointestinal endoscopy; Machine learning.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Endoscopy, Gastrointestinal
  • Gastroenterology*
  • Humans
  • Image Processing, Computer-Assisted
  • Machine Learning