Technological advances in artificial intelligence (AI) represent an enticing opportunity to benefit gastroenterological practice. Moreover, AI, through machine or deep learning, permits the ability to develop predictive models from large datasets. Possibilities of predictive model development in machine learning are numerous dependent on the clinical question. For example, binary classifiers aim to stratify allocation to a categorical outcome, such as the presence or absence of a gastrointestinal disease. In addition, continuous variable fitting techniques can be used to predict quantity of a therapeutic response, thus offering a tool to predict which therapeutic intervention may be most beneficial to the given patient. Namely, this permits an important opportunity for personalization of medicine, including a movement from guideline-specific treatment algorithms to patient-specific ones, providing both clinician and patient the capacity for data-driven decision making. Furthermore, such analyses could predict the development of GI disease prior to the manifestation of symptoms, raising the possibility of prevention or pre-treatment. In addition, computer vision additionally provides an exciting opportunity in endoscopy to automatically detect lesions. In this review, we overview the recent developments in healthcare-based AI and machine learning and describe promises and pitfalls for its application to gastroenterology.