Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit.

Gastrointest Endosc. 2020 Apr 25. pii: S0016-5107(20)34198-5. doi: 10.1016/j.gie.2020.04.044. [Epub ahead of print]

Parasa S1, Wallace M2, Bagci U3, Antonino M4, Berzin T5, Byrne M6, Celik H7, Farahani K8, Golding M4, Gross S9, Jamali V10, Mendonca P11, Mori Y12, Ninh A13, Repici A14, Rex D15, Skrinak K16, Thakkar SJ17, van Hooft JE18, Vargo J19, Yu H20, Xu Z21, Sharma P22.

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Abstract
 BACKGROUND AND AIMS:
 Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of gastrointestinal endoscopy in areas ranging from lesion detection and classification, to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more-efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in artificial intelligence research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology.
 METHODS:
 A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, and computer vision, Food and Drug Administration (FDA) and National Institutes of Health (NIH). Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology.
 RESULTS:
 There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision to endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems.
 CONCLUSIONS:
 Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, there are several constraints and areas that will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.