Literature DB >> 32565188

Position statement on priorities for artificial intelligence in GI endoscopy: a report by the ASGE Task Force.

Tyler M Berzin1, Sravanthi Parasa2, Michael B Wallace3, Seth A Gross4, Alessandro Repici5, Prateek Sharma6.   

Abstract

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.
Copyright © 2020 American Society for Gastrointestinal Endoscopy. Published by Elsevier Inc. All rights reserved.

Mesh:

Year:  2020        PMID: 32565188     DOI: 10.1016/j.gie.2020.06.035

Source DB:  PubMed          Journal:  Gastrointest Endosc        ISSN: 0016-5107            Impact factor:   9.427


  6 in total

Review 1.  Computer-Aided Diagnosis of Gastrointestinal Protruded Lesions Using Wireless Capsule Endoscopy: A Systematic Review and Diagnostic Test Accuracy Meta-Analysis.

Authors:  Hye Jin Kim; Eun Jeong Gong; Chang Seok Bang; Jae Jun Lee; Ki Tae Suk; Gwang Ho Baik
Journal:  J Pers Med       Date:  2022-04-17

2.  No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification.

Authors:  Eun Jeong Gong; Chang Seok Bang; Jae Jun Lee; Seung In Seo; Young Joo Yang; Gwang Ho Baik; Jong Wook Kim
Journal:  J Pers Med       Date:  2022-06-12

Review 3.  Quality indicators in esophagogastroduodenoscopy.

Authors:  Sang Yoon Kim; Jae Myung Park
Journal:  Clin Endosc       Date:  2022-05-16

Review 4.  Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications.

Authors:  James Weiquan Li; Lai Mun Wang; Tiing Leong Ang
Journal:  Singapore Med J       Date:  2022-03       Impact factor: 3.331

5.  Gastrointestinal Tract Disease Classification from Wireless Endoscopy Images Using Pretrained Deep Learning Model.

Authors:  J Yogapriya; Venkatesan Chandran; M G Sumithra; P Anitha; P Jenopaul; C Suresh Gnana Dhas
Journal:  Comput Math Methods Med       Date:  2021-09-11       Impact factor: 2.238

6.  Experimental evidence of effective human-AI collaboration in medical decision-making.

Authors:  Carlo Reverberi; Tommaso Rigon; Aldo Solari; Cesare Hassan; Paolo Cherubini; Andrea Cherubini
Journal:  Sci Rep       Date:  2022-09-02       Impact factor: 4.996

  6 in total

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