The Legal and Ethical Framework for Artificial Intelligence in Gastrointestinal Endoscopy: A World Endoscopy Organization International Consensus Statement
Bibliographic record
Abstract
The OperA (Optimising Colorectal Cancer Prevention through Personalized Treatment with Artificial Intelligence) project aims to transform colorectal cancer care through artificial intelligence (AI) innovations. Recognizing that legal and ethical challenges remain key obstacles to clinical integration, this Delphi study sought to identify and prioritize such concerns in the context of gastrointestinal (GI) endoscopy. Fourteen international experts participated in a 2-round Delphi process. In round 1, the steering committee, with feedback from participants, proposed legal and ethical issues pertaining to AI in endoscopy. Round 2 involved iterative rating and refinement of these issues to achieve consensus on their importance. Consensus was reached on 10 key statements spanning 3 thematic domains: data governance, medicolegal implications, and equity and bias. Experts emphasized the need for robust data protection, transparent algorithmic development, and institutional clarity on data ownership. Liability concerns related to AI-assisted diagnosis and automated reporting were highlighted, alongside calls for guidance from legal and professional bodies. Finally, participants underscored the importance of demographic diversity in training data sets and transparent reporting practices to mitigate bias and ensure equitable AI deployment. As AI tools become increasingly integrated into the clinical practice of gastroenterology, addressing legal, ethical, and equity-related challenges is essential. This expert consensus provides a foundation for developing guidelines and regulatory frameworks to support responsible AI adoption in GI endoscopy.
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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 | 0.305 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.008 | 0.021 |
| Research integrity | 0.024 | 0.035 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".