TY - JOUR
T1 - Position statement on priorities for artificial intelligence in GI endoscopy
T2 - a report by the ASGE Task Force
AU - Berzin, Tyler M.
AU - Parasa, Sravanthi
AU - Wallace, Michael B.
AU - Gross, Seth A.
AU - Repici, Alessandro
AU - Sharma, Prateek
N1 - Publisher Copyright:
© 2020 American Society for Gastrointestinal Endoscopy
PY - 2020/10
Y1 - 2020/10
N2 - Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes.
AB - Artificial intelligence (AI) in GI endoscopy holds tremendous promise to augment clinical performance, establish better treatment plans, and improve patient outcomes. Although there are promising initial applications and preliminary clinical data for AI in gastroenterology, the field is still in a very early phase, with limited clinical use. The American Society for Gastrointestinal Endoscopy has convened an AI Task Force to develop guidance around clinical implementation, testing/validating algorithms, and building pathways for successful implementation of AI in GI endoscopy. This White Paper focuses on 3 areas: (1) priority use cases for development of AI algorithms in GI, both for specific clinical scenarios and for streamlining clinical workflows, quality reporting, and practice management; (2) data science priorities, including development of image libraries, and standardization of methods for storing, sharing, and annotating endoscopic images/video; and (3) research priorities, focusing on the importance of high-quality, prospective trials measuring clinically meaningful patient outcomes.
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U2 - 10.1016/j.gie.2020.06.035
DO - 10.1016/j.gie.2020.06.035
M3 - Article
C2 - 32565188
AN - SCOPUS:85089588670
SN - 0016-5107
VL - 92
SP - 951
EP - 959
JO - Gastrointestinal endoscopy
JF - Gastrointestinal endoscopy
IS - 4
ER -