Toward a Human-Centered AI-assisted Colonoscopy System in Australia
It addresses the problem of ineffective clinical integration of AI in colonoscopy for healthcare providers and patients, highlighting an incremental focus on human-centered design.
This paper identifies a disconnect in AI-assisted colonoscopy development, where current efforts prioritize machine learning model performance over user interface design, workflow integration, and user experience, based on an Australian field study with observations and gastroenterologist interviews.
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.