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VOLUME 10 , ISSUE 2 ( July-December, 2020 ) > List of Articles
Prajna Anirvan, Dinesh Meher, Shivaram P Singh
Keywords : Artificial intelligence, Automated detection, Computer-aided detection, Deep learning, Developing countries, Lesion detection, Health resources, Health services accessibility
Citation Information : Anirvan P, Meher D, Singh SP. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check. Euroasian J Hepatogastroenterol 2020; 10 (2):92-97.
License: CC BY-NC 4.0
Published Online: 06-01-2021
Copyright Statement: Copyright © 2020; Jaypee Brothers Medical Publishers (P) Ltd.
Artificial intelligence (AI) is being increasingly explored in different domains of gastroenterology, particularly in endoscopic image analysis, cancer screening, and prognostication models. It is widely touted to become an integral part of routine endoscopies, considering the bulk of data handled by endoscopists and the complex nature of critical analyses performed. However, the application of AI in endoscopy in resource-constrained settings remains fraught with problems. We conducted an extensive literature review using the PubMed database on articles covering the application of AI in endoscopy and the difficulties encountered in resource-constrained settings. We have tried to summarize in the present review the potential problems that may hinder the application of AI in such settings. Hopefully, this review will enable endoscopists and health policymakers to ponder over these issues before trying to extrapolate the advancements of AI in technically advanced settings to those having constraints at multiple levels.
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