Results of the Ninth Scientific Workshop of the European Crohn’s and Colitis Organisation (ECCO): artificial intelligence in IBD: regulatory and methodological considerations
Bibliographic record
Abstract
With the rapid growth of artificial intelligence (AI) applications in the field of inflammatory bowel disease (IBD), an increasing number of regulatory and methodological considerations have become apparent. Currently, there remains much uncertainty and limited experience in the field of IBD regarding some of the regulatory and methodological pitfalls to be considered when developing and deploying AI applications for positive clinical and health system impact. Accordingly, an expert panel was convened by the European Crohn's and Colitis Organisation to review the published literature and provide an overview of key regulatory aspects for the application of AI in IBD. This article discusses and, where possible, provides guidance on key methodological and regulatory considerations for AI in IBD. Topics covered include: potential clinical application-focused algorithm design; ethical, moral and legal considerations; regulatory agency perspectives; an overview of regulatory submission and consideration of reimbursement. By providing clinicians with a primer to key regulatory and methodological considerations, we hope to accelerate knowledge translation and implementation of AI-enabled digital health innovations in clinical practice and ultimately improve outcomes for people living with and caring for those living with IBD.
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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 | 0.117 | 0.119 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.019 | 0.020 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".