Toward a Human-Centered AI-assisted Colonoscopy System in Australia
Chen, Hsiang-Ting, Zhang, Yuan, Carneiro, Gustavo, Singh, Rajvinder
–arXiv.org Artificial Intelligence
While AI-assisted colonoscopy promises improved colorectal cancer screening, its success relies on effective integration into clinical practice, not just algorithmic accuracy. This paper, based on an Australian field study (observations and gastroenterologist interviews), highlights a critical disconnect: current development prioritizes machine learning model performance, overlooking essential aspects of user interface design, workflow integration, and overall user experience. Industry interactions reveal a similar emphasis on data and algorithms. To realize AI's full potential, the HCI community must champion user-centered design, ensuring these systems are usable, support endoscopist expertise, and enhance patient outcomes.
arXiv.org Artificial Intelligence
Mar-15-2025
- Country:
- Asia > Japan
- Honshū > Kantō > Kanagawa Prefecture > Yokohama (0.06)
- Europe > United Kingdom
- North America > United States
- Louisiana > Orleans Parish
- New Orleans (0.04)
- New York > New York County
- New York City (0.04)
- Louisiana > Orleans Parish
- Oceania > Australia
- South Australia > Adelaide (0.05)
- Asia > Japan
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- Research Report > Experimental Study (0.47)
- Industry:
- Health & Medicine > Therapeutic Area
- Gastroenterology (1.00)
- Oncology > Colorectal Cancer (1.00)
- Health & Medicine > Therapeutic Area
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