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Artificial intelligence in biliopancreatic endoscopy: Is there any role?

Ahmad, OF; Stassen, P; Webster, GJ; (2021) Artificial intelligence in biliopancreatic endoscopy: Is there any role? Best Practice & Research Clinical Gastroenterology , 52-53 , Article 101724. 10.1016/j.bpg.2020.101724. Green open access

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Abstract

Artificial intelligence (AI) research in endoscopy is being translated at rapid pace with a number of approved devices now available for use in luminal endoscopy. However, the published literature for AI in biliopancreatic endoscopy is predominantly limited to early pre-clinical studies including applications for diagnostic EUS and patient risk stratification. Potential future use cases are highlighted in this manuscript including optical characterisation of strictures during cholangioscopy, prediction of post-ERCP acute pancreatitis and selective biliary duct cannulation difficulty, automated report generation and novel AI-based quality key performance metrics. To realise the full potential of AI and accelerate innovation, it is crucial that robust inter-disciplinary collaborations are formed between biliopancreatic endoscopists and AI researchers.

Type: Article
Title: Artificial intelligence in biliopancreatic endoscopy: Is there any role?
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.bpg.2020.101724
Publisher version: https://doi.org/10.1016/j.bpg.2020.101724
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Artificial intelligence; Machine learning; Endoscopic retrograde cholangiopancreatography; Endoscopic ultrasonography
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10120844
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