mlsanityData to 14 Oct 2026

Toward a Human-Centered AI-assisted Colonoscopy System in Australia

arXiv 2503.20790 ↗First posted 15 Mar 2025cs.HC cs.AIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 3 of its 36 references that are in the map. First posted on arXiv 15 Mar 2025.

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3 of 36 references.

References · 3

  1. 2018Deep Learning for Generic Object Detection: A Surveycited by 392
  2. 2021"Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deploymentcited by 28
  3. 2022In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videoscited by 2

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