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Review
. 2020 Jul-Dec;10(2):92-97.
doi: 10.5005/jp-journals-10018-1322.

Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check

Prajna Anirvan  1 , Dinesh Meher  1 , Shivaram P Singh  1
Affiliations
Free PMC article
Review

Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check

Prajna Anirvan et al. Euroasian J Hepatogastroenterol. 2020 Jul-Dec.
Free PMC article

Abstract

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. How to cite this article: Anirvan P, Meher D, Singh SP. Artificial Intelligence in Gastrointestinal Endoscopy in a Resource-constrained Setting: A Reality Check. Euroasian J Hepato-Gastroenterol 2020;10(2): 92-97.

Keywords: Artificial intelligence; Automated detection; Computer-aided detection; Deep learning; Developing countries; Health resources; Health services accessibility; Lesion detection.

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Conflict of interest statement

Source of support: Nil Conflict of interest: None

Figures

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ML and DL—subsets of AI
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Deep learning—a diagrammatic representation
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Fig. 3
Image analysis using AI
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Fig. 4
Potential problems in implementing AI in endoscopy in a resource-constrained setting
See this image and copyright information in PMC

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