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.
Highlights
Artificial intelligence (AI)-based technologies are being developed at a rapid pace for gastrointestinal endoscopy, in particular for 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
How do we improve the performance of AI/computer-aided diagnosis/detection (CAD) to detect more challenging and advanced lesions (e. g. subtle flat lesions and sessile serrated lesions)?
Summary
Artificial intelligence (AI)-based technologies are being developed at a rapid pace for gastrointestinal endoscopy, in particular for colonoscopy. The vast majority of AI research in endoscopy to date, and more broadly within healthcare, has focused on preclinical or retrospective studies. These studies have been crucial in the early phase of development [2]. For example by the National Institutes of Health and Radiological Society of North America, to identify key research priorities for AI in medical imaging, this focused predominantly on foundational AI research topics, such as the development of new image reconstruction methods and novel machine-learning algorithms tailored to clinical imaging data [10]. Artificial intelligence (AI) research in colonoscopy is progressing rapidly but widespread clinical implementation is not yet a reality.
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