Patients’ sentiments on artificial intelligence in endoscopy: A large-scale intercontinental opinion survey
Bibliographic record
Abstract
Aims In recent years, the number of clinical studies evaluating artificial intelligence (AI) systems in endoscopy has increased. Authorities encourage integration of patients’ thoughts in development of innovative medical interventions to allow their patient-friendly implementation. However, little is known about patient perception regarding AI in endoscopy. Methods A prospective questionnaire study was conducted as part of the World Endoscopy Organization (WEO) AI committee activities. The committee developed 13 statements on the use of AI in endoscopy which were distributed to patients using a dedicated online survey platform. To avoid potential selection bias, the questionnaires were distributed equally to each of the six continents in the World. Patients 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, 1,237 patients completed the survey (>200 per continent). The majority of patients believed that humans and AI can complement each other (74.3% agreed) and would support its use (75.5% agreed). However, fewer patients believed that an AI system could be better than experienced endoscopists (38.2% agreed) and endoscopists should remain responsible for decision making (92.3% agreed). The majority of patients believed that endoscopists or hospitals should be liable for medical malpractice induced by the use of AI (76.9% agreed). Conclusions This large-scale international survey performed by the WEO AI committee revealed an obvious trend that patients appreciated benefit of using AI in endoscopy but did not blindly rely on the technology, leaving endoscopists and hospitals responsible for decision making and liability issues. 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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".