Results of the Ninth Scientific Workshop of the European Crohn’s and Colitis Organisation (ECCO): Artificial Intelligence in medical management and precision medicine
Bibliographic record
Abstract
BACKGROUND AND AIMS: Artificial intelligence (AI) is increasingly being applied in various fields of medicine, including inflammatory bowel diseases (IBD). This systematic review, conducted as part of the ECCO 9th Scientific Workshop on AI in IBD, explores AI applications in multiomics precision medicine, large language models (LLMs) for textual tasks, and utilization of wearable and remote care technologies. METHODS: A comprehensive systematic analysis of the literature was undertaken, emphasizing three topics: multiomics predictive models in IBD; natural language processing (NLP) and LLMs for clinical practice, research and patient communication; and the role of remote monitoring and wearable devices. RESULTS: Key areas of promise include the implementation of NLP and LLMs for case identification and differentiation, tracking disease activity, pharmacovigilance, quality assurance, and patient support. Multiomic approaches, integrating genomics, transcriptomics, proteomics, metabolomics, and metagenomics, show potential for developing more accurate diagnostic and risk prediction models and improving treatment response prediction and detection of actionable drug targets for future therapeutics. Wearables and remote monitoring technologies can transform IBD management from episodic assessments to continuous, less biased tracking of patient-reported outcomes and physiological biomarkers. CONCLUSIONS: While AI and multiomics approaches hold substantial promise for advancing IBD management and research, further refinement is necessary to ensure content validity and address safety concerns, thereby allowing integration of AI into clinical workflows and safeguarding of data privacy. Future research should prioritize the integration of diverse omic data, conduct of longitudinal studies, and validation in large and diverse cohorts.
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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 | 0.075 | 0.112 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".