Expert-AI Collaborative Training for Novice Endoscopists: A Path to Enhanced Efficiency
The AI-assisted EndoAdd system improved novice endoscopist performance, reducing blind spots and enhancing lesion detection. AI systems like EndoAdd show potential in accelerating endoscopy training and improving procedural quality.
Authors
- Huankai Shou
- Wen‐Zheng Qin
- Ping‐Hong Zhou (ORCID: https://orcid.org/0000-0002-5434-0540)
- Pei‐Yao Fu (ORCID: https://orcid.org/0000-0002-8816-651X)
- Quan‐Lin Li (ORCID: https://orcid.org/0000-0002-9108-8786)
- Shuo Wang (ORCID: https://orcid.org/0000-0002-2947-8783)
- Zhen Zhang (ORCID: https://orcid.org/0000-0002-7603-3824)
- Zihan Geng (ORCID: https://orcid.org/0000-0003-2690-3374)
- Yan Zhu
- Pingting Gao (ORCID: https://orcid.org/0000-0001-8208-3134)
- Ling Du (ORCID: https://orcid.org/0000-0002-3019-4315)
- Xin‐Yang Liu
- B. Chen
- Meng‐Jiang He
Institutions
- Shanghai Medical College of Fudan University (CN)
- Fudan University (CN)
- Zhongshan Hospital of Xiamen University (CN)
- Zhongshan Hospital (CN)
- Imperial College London (GB)
Publication Details
- Journal
- Bioengineering
- Published
- 2025-05-28
- DOI
- https://doi.org/10.3390/bioengineering12060582
- Citations
- 2
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Fujian Province