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

arXiv: https://arxiv.org/abs/2503.20790 · HTML: https://mlsanity.com/paper/2503.20790/

## 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.

## Builds on (most cited first, 3 of 3)

- [Deep Learning for Generic Object Detection: A Survey](https://mlsanity.com/paper/1809.02165/) (2018)
- ["Brilliant AI Doctor" in Rural China: Tensions and Challenges in AI-Powered CDSS Deployment](https://mlsanity.com/paper/2101.01524/) (2021)
- [In Defense of Kalman Filtering for Polyp Tracking from Colonoscopy Videos](https://mlsanity.com/paper/2201.11450/) (2022)

## 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.