TY - JOUR
T1 - Clinicians’ Guide to Artificial Intelligence in Colon Capsule Endoscopy
T2 - Technology Made Simple
AU - Lei, Ian I.
AU - Nia, Gohar J.
AU - White, Elizabeth
AU - Wenzek, Hagen
AU - Segui, Santi
AU - Watson, Angus J.M.
AU - Koulaouzidis, Anastasios
AU - Arasaradnam, Ramesh P.
PY - 2023/3
Y1 - 2023/3
N2 - 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.
AB - 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.
KW - artificial intelligence (AI)
KW - colon capsule endoscopy (CCE)
KW - convolutional neural networks (CNN)
KW - decision-making systems (DMS)
KW - deep learning (DL)
KW - machine learning (ML)
UR - http://www.scopus.com/inward/record.url?scp=85151749179&partnerID=8YFLogxK
U2 - 10.3390/diagnostics13061038
DO - 10.3390/diagnostics13061038
M3 - Review article
AN - SCOPUS:85151749179
SN - 2075-4418
VL - 13
JO - Diagnostics
JF - Diagnostics
IS - 6
M1 - 1038
ER -