Visual gaze patterns in trainee endoscopists – a novel assessment tool

Karamchandani, Urvi; Erridge, Simon; Evans-Harvey, Keane; Darzi, Ara; Hoare, Jonathan; Sodergren, Mikael H.

doi:10.1080/00365521.2022.2064723

Cited by 4 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visual gaze pattern (VGP) is an important metric in vigilance tasks including detection of pathology during endoscopy, with substantial differences between VGPs of experts and those of non-experts[ 66 , 76 - 78 ]. Analysis of the VGP of endoscopists with high ADRs showed a positive correlation with VGPs that tracked the periphery of the bowel wall and the periphery of the screen in a ‘bottom U’ configuration during colonoscopy[ 76 , 79 ].…”

Section: Human Factors Influencing User Interaction With Aimentioning

confidence: 99%

Human-artificial intelligence interaction in gastrointestinal endoscopy

Campion,

O'Connor,

Lahiff

2024

World J Gastrointest Endosc

View full text Add to dashboard Cite

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.

show abstract