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Frontiers in Intelligent Colonoscopy
Ji, Ge-Peng Liu, Jingyi Xu, Peng Barnes, Nick Khan, Fahad Shahbaz Khan, Salman Fan, Deng-Ping
Ji, Ge-Peng
Liu, Jingyi
Xu, Peng
Barnes, Nick
Khan, Fahad Shahbaz
Khan, Salman
Fan, Deng-Ping
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s11633-025-1597-6.pdf
Adobe PDF, 11.2 MB
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Computer Vision
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Journal article
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http://creativecommons.org/licenses/by/4.0/
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English
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Abstract
Colonoscopy is currently one of the most sensitive screening methods for colorectal cancer. This study investigates the frontiers of intelligent colonoscopy techniques and their prospective implications for multimodal medical applications. With this goal, we begin by assessing the current data-centric and model-centric landscapes through four tasks for colonoscopic scene perception, including classification, detection, segmentation, and vision-language understanding. Our assessment reveals domain-specific challenges and underscores the need for further multimodal research in colonoscopy. To address these gaps, we establish three foundational initiatives: a large-scale multimodal instruction tuning dataset ColonINST, a colonoscopy-designed multimodal language model ColonGPT, and a multimodal benchmark. To facilitate continuous advancements in this rapidly evolving field, we provide a public website for the latest updates: https://github.com/ai4colonoscopy/IntelliScope.
Citation
Ji, G.P., Liu, J., Xu, P., Barnes, N., Khan, F.S., Khan, S., Fan, D.P. (2026). Frontiers in Intelligent Colonoscopy. Machine Intelligence Research, 1-45. https://doi.org/10.1007/s11633-025-1597-6
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Machine Intelligence Research
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Keywords
Colonoscopy survey, polyp segmentation, multimodal large language model, multimodal benchmark, healthcare AI
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Springer Nature