Academic Journal
Expert-AI Collaborative Training for Novice Endoscopists: A Path to Enhanced Efficiency.
| Τίτλος: | Expert-AI Collaborative Training for Novice Endoscopists: A Path to Enhanced Efficiency. |
|---|---|
| Συγγραφείς: | Zhang Z; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Chen BS; Endoscopic Center, Zhongshan Hospital (Xiamen), Fudan University, Xiamen 201104, China., Du L; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Li QL; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Zhu Y; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Fu PY; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Qin WZ; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Shou HK; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Gao PT; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Liu XY; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., He MJ; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Geng ZH; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China., Wang S; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China.; Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China.; Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China.; Data Science Institute, Imperial College, London SW7 2AZ, UK., Zhou PH; Endoscopy Center, Endoscopy Research Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China.; Shanghai Collaborative Innovation Center of Endoscopy, Shanghai 200032, China. |
| Πηγή: | Bioengineering (Basel, Switzerland) [Bioengineering (Basel)] 2025 May 28; Vol. 12 (6). Date of Electronic Publication: 2025 May 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101676056 Publication Model: Electronic Cited Medium: Print ISSN: 2306-5354 (Print) Linking ISSN: 23065354 NLM ISO Abbreviation: Bioengineering (Basel) Subsets: PubMed not MEDLINE |
| Imprint Name(s): | Original Publication: Basel, Switzerland : MDPI AG, [2014]- |
| Περίληψη: | Background: Esophagogastroduodenoscopy (EGD) is essential for diagnosing upper gastrointestinal disorders. Traditional training for novice endoscopists is often inefficient and inconsistent. This study evaluates the effectiveness of an AI-assisted system (EndoAdd) in improving EGD training. Methods: In a randomized controlled trial, eight novice endoscopists were assigned to either the EndoAdd group or a control group (traditional training). The EndoAdd system provided real-time feedback on blind spots and photodocumentation. Primary outcomes were the number of blind spots, with secondary outcomes including examination time, lesion detection, and photodocumentation completeness. Results: The EndoAdd system exhibited an overall accuracy of 98.0% and a mean area under the curve (AUC) of 0.984. The EndoAdd group had significantly fewer blind spots, improved photodocumentation, and a higher lesion detection rate. Examination time was reduced without compromising diagnostic accuracy. Conclusions: 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. |
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| Contributed Indexing: | Keywords: artificial intelligence; novice endoscopists; training |
| Entry Date(s): | Date Created: 20250626 Latest Revision: 20250628 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12189645 |
| DOI: | 10.3390/bioengineering12060582 |
| PMID: | 40564399 |
| Βάση Δεδομένων: | MEDLINE |
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