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Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple

Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy....

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Autores principales: Lei, Ian I., Nia, Gohar J., White, Elizabeth, Wenzek, Hagen, Segui, Santi, Watson, Angus J. M., Koulaouzidis, Anastasios, Arasaradnam, Ramesh P.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047552/
https://www.ncbi.nlm.nih.gov/pubmed/36980347
http://dx.doi.org/10.3390/diagnostics13061038
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author Lei, Ian I.
Nia, Gohar J.
White, Elizabeth
Wenzek, Hagen
Segui, Santi
Watson, Angus J. M.
Koulaouzidis, Anastasios
Arasaradnam, Ramesh P.
author_facet Lei, Ian I.
Nia, Gohar J.
White, Elizabeth
Wenzek, Hagen
Segui, Santi
Watson, Angus J. M.
Koulaouzidis, Anastasios
Arasaradnam, Ramesh P.
author_sort Lei, Ian I.
collection PubMed
description Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings.
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spelling pubmed-100475522023-03-29 Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple Lei, Ian I. Nia, Gohar J. White, Elizabeth Wenzek, Hagen Segui, Santi Watson, Angus J. M. Koulaouzidis, Anastasios Arasaradnam, Ramesh P. Diagnostics (Basel) Review Artificial intelligence (AI) applications have become widely popular across the healthcare ecosystem. Colon capsule endoscopy (CCE) was adopted in the NHS England pilot project following the recent COVID pandemic’s impact. It demonstrated its capability to relieve the national backlog in endoscopy. As a result, AI-assisted colon capsule video analysis has become gastroenterology’s most active research area. However, with rapid AI advances, mastering these complex machine learning concepts remains challenging for healthcare professionals. This forms a barrier for clinicians to take on this new technology and embrace the new era of big data. This paper aims to bridge the knowledge gap between the current CCE system and the future, fully integrated AI system. The primary focus is on simplifying the technical terms and concepts in machine learning. This will hopefully address the general “fear of the unknown in AI” by helping healthcare professionals understand the basic principle of machine learning in capsule endoscopy and apply this knowledge in their future interactions and adaptation to AI technology. It also summarises the evidence of AI in CCE and its impact on diagnostic pathways. Finally, it discusses the unintended consequences of using AI, ethical challenges, potential flaws, and bias within clinical settings. MDPI 2023-03-08 /pmc/articles/PMC10047552/ /pubmed/36980347 http://dx.doi.org/10.3390/diagnostics13061038 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Lei, Ian I.
Nia, Gohar J.
White, Elizabeth
Wenzek, Hagen
Segui, Santi
Watson, Angus J. M.
Koulaouzidis, Anastasios
Arasaradnam, Ramesh P.
Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
title Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
title_full Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
title_fullStr Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
title_full_unstemmed Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
title_short Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy—Technology Made Simple
title_sort clinicians’ guide to artificial intelligence in colon capsule endoscopy—technology made simple
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10047552/
https://www.ncbi.nlm.nih.gov/pubmed/36980347
http://dx.doi.org/10.3390/diagnostics13061038
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