Establishing key research questions for the implementation of artificial intelligence in colonoscopy: a modified Delphi method

Abstract Background  Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods  An established modified Delphi approach for research priority settin...

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Published in:Endoscopy Vol. 53; no. 9; pp. 893 - 901
Main Authors: Ahmad, Omer F., Mori, Yuichi, Misawa, Masashi, Kudo, Shin-ei, Anderson, John T., Bernal, Jorge, Berzin, Tyler M., Bisschops, Raf, Byrne, Michael F., Chen, Peng-Jen, East, James E., Eelbode, Tom, Elson, Daniel S., Gurudu, Suryakanth R., Histace, Aymeric, Karnes, William E., Repici, Alessandro, Singh, Rajvinder, Valdastri, Pietro, Wallace, Michael B., Wang, Pu, Stoyanov, Danail, Lovat, Laurence B.
Format: Journal Article
Language:English
Published: Rüdigerstraße 14, 70469 Stuttgart, Germany Georg Thieme Verlag KG 01.09.2021
Subjects:
Artificial Intelligence
Colonoscopy
Delphi Technique
Humans
Original article
ISSN:0013-726X, 1438-8812, 1438-8812
Online Access:Get full text
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Summary:Abstract Background  Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality. We aimed to identify the top implementation research priorities. Methods  An established modified Delphi approach for research priority setting was used. Fifteen international experts, including endoscopists and translational computer scientists/engineers, from nine countries participated in an online survey over 9 months. Questions related to AI implementation in colonoscopy were generated as a long-list in the first round, and then scored in two subsequent rounds to identify the top 10 research questions. Results  The top 10 ranked questions were categorized into five themes. Theme 1: clinical trial design/end points (4 questions), related to optimum trial designs for polyp detection and characterization, determining the optimal end points for evaluation of AI, and demonstrating impact on interval cancer rates. Theme 2: technological developments (3 questions), including improving detection of more challenging and advanced lesions, reduction of false-positive rates, and minimizing latency. Theme 3: clinical adoption/integration (1 question), concerning the effective combination of detection and characterization into one workflow. Theme 4: data access/annotation (1 question), concerning more efficient or automated data annotation methods to reduce the burden on human experts. Theme 5: regulatory approval (1 question), related to making regulatory approval processes more efficient. Conclusions  This is the first reported international research priority setting exercise for AI in colonoscopy. The study findings should be used as a framework to guide future research with key stakeholders to accelerate the clinical implementation of AI in endoscopy.
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ISSN:0013-726X
1438-8812
1438-8812
DOI:10.1055/a-1306-7590