Artificial intelligence-assisted endoscopy and examiner confidence : a study on human–artificial intelligence interaction in Barrett's Esophagus (With Video)

  • Objective Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall short in clinical applications due to human-AI interaction factors. This video-based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes ofObjective Despite high stand-alone performance, studies demonstrate that artificial intelligence (AI)-supported endoscopic diagnostics often fall short in clinical applications due to human-AI interaction factors. This video-based trial on Barrett's esophagus aimed to investigate how examiner behavior, their levels of confidence, and system usability influence the diagnostic outcomes of AI-assisted endoscopy. Methods The present analysis employed data from a multicenter randomized controlled tandem video trial involving 22 endoscopists with varying degrees of expertise. Participants were tasked with evaluating a set of 96 endoscopic videos of Barrett's esophagus in two distinct rounds, with and without AI assistance. Diagnostic confidence levels were recorded, and decision changes were categorized according to the AI prediction. Additional surveys assessed user experience and system usability ratings. Results AI assistance significantly increased examiner confidence levels (p < 0.001) and accuracy. Withdrawing AI assistance decreased confidence (p < 0.001), but not accuracy. Experts consistently reported higher confidence than non-experts (p < 0.001), regardless of performance. Despite improved confidence, correct AI guidance was disregarded in 16% of all cases, and 9% of initially correct diagnoses were changed to incorrect ones. Overreliance on AI, algorithm aversion, and uncertainty in AI predictions were identified as key factors influencing outcomes. The System Usability Scale questionnaire scores indicated good to excellent usability, with non-experts scoring 73.5 and experts 85.6. Conclusions Our findings highlight the pivotal function of examiner behavior in AI-assisted endoscopy. To fully realize the benefits of AI, implementing explainable AI, improving user interfaces, and providing targeted training are essential. Addressing these factors could enhance diagnostic accuracy and confidence in clinical practice.show moreshow less

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Author:David Roser, Michael MeinikheimORCiD, Anna Muzalyova, Robert Mendel, Christoph PalmORCiDGND, Andreas Probst, Sandra Nagl, Markus W. Scheppach, Christoph RömmeleORCiD, Elisabeth Schnoy, Nasim Parsa, Michael F. Byrne, Helmut Messmann, Alanna EbigboORCiD
DOI:https://doi.org/10.1002/deo2.70150
Parent Title (English):DEN Open
Publisher:Wiley
Document Type:Article
Language:English
Year of first Publication:2025
Release Date:2025/08/12
Volume:6
Issue:1
Article Number:e70150
Pagenumber:8
Institutes:Fakultät Informatik und Mathematik
Fakultät Informatik und Mathematik / Labor Regensburg Medical Image Computing (ReMIC)
Begutachtungsstatus:peer-reviewed
Open Access Publication channel:Gold Open Access- Erstveröffentlichung in einem/als Open-Access-Medium
research focus:Lebenswissenschaften und Ethik
Licence (German):Creative Commons - CC BY - Namensnennung 4.0 International
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