Endoscopist sentiments on artificial intelligence in endoscopy: A Large-scale intercontinental opinion survey
Bibliographic record
Abstract
Aims In recent years, there has been rapid development of AI devices in endoscopy. Little is however known about endoscopist perception regarding AI in clinical practice. The aim of this study was to undertake an international endoscopist opinion survey on behalf of the World Endoscopy Organization AI committee about the use of artificial intelligence. Methods A prospective questionnaire study was conducted as part of the World Endoscopy Organization AI committee activities. The committee developed 23 statements on the use of AI in endoscopy which were distributed to endoscopists using an online survey platform. Endoscopists responded to each of the statements by using a 5-point Likert-scale, ranging from strongly disagree (1) to strongly agree (5). Results In total, 465 endoscopists completed the survey (from 83 countries). The endoscopists background was general (36%), specialist interventional (55%) and trainee (9%). 39% had experience of using AI in clinical practice. The majority believed that AI would improve endoscopy quality (90%), efficiency (87%) and patient outcomes (86%). Most thought that CADe would help detect clinically relevant polyps (86%) and upper GI neoplasia (87%). 68% felt more comfortable leaving in a diminutive rectosigmoid polyp with CADx assistance. Financial re-imbursement was considered important (72%) and most were concerned about medical liability (47%), with a mixed view on accountability (endoscopist 57%, manufacturer 22% and hospital 8%). Potential de-skilling was a concern (37%). A small majority felt that AI would lead to unnecessary resections and biopsies in the upper and lower GI tract, and also lengthen procedure times. Overall, the majority would use upper GI CADe (90%), lower GI CADe (83%) and CADx (87%) when available. Conclusions In this international endoscopist survey, participants strongly believed that AI would improve endoscopy quality and outcomes. Financial re-imbursement, concerns about medical liability and potential de-skilling are important issues. A small majority felt that AI would lead to unnecessary resections and increase procedure times. Overall, the vast majority would use AI when available. Publication History Article published online: 15 April 2024 © 2024. European Society of Gastrointestinal Endoscopy. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".