Campion JR, O'Connor DB, Lahiff C. Human-artificial intelligence interaction in gastrointestinal endoscopy. World J Gastrointest Endosc 2024; 16(3): 126-135 [PMID: 38577646 DOI: 10.4253/wjge.v16.i3.126]
Corresponding Author of This Article
John R Campion, MB BCh BAO, MSc, MA, MRCPI, Doctor, Research Fellow, Department of Gastroenterology, Mater Misericordiae University Hospital, Eccles St, Dublin D07 AX57, Ireland. johncampion@eril.ie
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Minireviews
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastrointest Endosc. Mar 16, 2024; 16(3): 126-135 Published online Mar 16, 2024. doi: 10.4253/wjge.v16.i3.126
Human-artificial intelligence interaction in gastrointestinal endoscopy
John R Campion, Donal B O'Connor, Conor Lahiff
John R Campion, Conor Lahiff, Department of Gastroenterology, Mater Misericordiae University Hospital, Dublin D07 AX57, Ireland
John R Campion, Conor Lahiff, School of Medicine, University College Dublin, Dublin D04 C7X2, Ireland
Donal B O'Connor, Department of Surgery, Trinity College Dublin, Dublin D02 R590, Ireland
Author contributions: Campion JR designed and drafted the original manuscript and reviewed all subsequent and final drafts; O'Connor DB drafted the manuscript and reviewed the draft and final manuscripts; Lahiff C designed and reviewed the original manuscript; all subsequent drafts, including the final draft.
Conflict-of-interest statement: Authors declare no conflict of interests for this article. O'Connor DB is employed by the HPRA in Ireland, a government agency for medical device regulation in the EU and has no conflicts relevant to this article.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: John R Campion, MB BCh BAO, MSc, MA, MRCPI, Doctor, Research Fellow, Department of Gastroenterology, Mater Misericordiae University Hospital, Eccles St, Dublin D07 AX57, Ireland. johncampion@eril.ie
Received: December 31, 2023 Peer-review started: December 31, 2023 First decision: January 16, 2024 Revised: January 18, 2024 Accepted: February 23, 2024 Article in press: February 23, 2024 Published online: March 16, 2024
Abstract
The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human–AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.
Core Tip: As an endoscopist you should familiarise yourself with the capabilities, strengths and weaknesses of any artificial intelligence (AI) technology you intend to use. It is important to be cognisant of the human factors and psychological biases that influence how you as an individual user treat advice from AI platforms. Those using AI technologies in healthcare should be involved in the development of those technologies and should advocate for a human-centred approach to their design and implementation.
