Berkel, NV;
Ahmad, OF;
Stoyanov, D;
Lovat, L;
Blandford, A;
(2020)
Designing Visual Markers for Continuous Artificial Intelligence Support.
ACM Transactions on Computing for Healthcare
, 2
(1)
pp. 1-24.
10.1145/3422156.
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Colonoscopy_visual_markers.pdf - Accepted Version Download (4MB) | Preview |
Abstract
Colonoscopy, the visual inspection of the large bowel using an endoscope, offers protection against colorectal cancer by allowing for the detection and removal of pre-cancerous polyps. The literature on polyp detection shows widely varying miss rates among clinicians, with averages ranging around 22%--27%. While recent work has considered the use of AI support systems for polyp detection, how to visualise and integrate these systems into clinical practice is an open question. In this work, we explore the design of visual markers as used in an AI support system for colonoscopy. Supported by the gastroenterologists in our team, we designed seven unique visual markers and rendered them on real-life patient video footage. Through an online survey targeting relevant clinical staff (N = 36), we evaluated these designs and obtained initial insights and understanding into the way in which clinical staff envision AI to integrate in their daily work-environment. Our results provide concrete recommendations for the future deployment of AI support systems in continuous, adaptive scenarios.
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