How Ready Are Machine-Learning Prognostic Models for Inflammatory Bowel Disease? A Systematic Review and PROBAST + AI Appraisal of 111 Studies
TL;DR
Until calibration, decision-curve utility, and transportability are reported alongside external validation, clinical deployment remains premature, according to the evidence base maps.
Abstract
Background: Artificial intelligence (AI) and machine-learning (ML) prognostic models are increasingly developed for inflammatory bowel disease (IBD), yet their reported performance and clinical readiness remain inadequately appraised. Methods: Following PRISMA 2020 and a registered protocol, we searched PubMed, Web of Science, IEEE Xplore, and arXiv (January 2012–January 2026) for studies developing or validating prognostic models in Crohn’s disease or ulcerative colitis. Two reviewers independently screened the studies, extracted data, and assessed risk of bias using PROBAST + AI; discrimination was summarized by area under the curve (AUC) and stratified by validation type. Results: Of the 3050 records, 111 studies were included. Treatment response was the most common target; laboratory data and electronic health records were the most frequent modalities. Across 83 studies, the median AUC was 0.850; externally validated models reached 0.870 versus 0.845 for internal-only and 0.790 for cross-validation-only. External validation was reported in 29.7%, calibration in 14.4% and analysis code in 3.6%; the analysis domain was the leading source of bias. Conclusions: The evidence base maps reported discrimination rather than demonstrated clinical readiness. Until calibration, decision-curve utility, and transportability are reported alongside external validation, clinical deployment remains premature.