Machine learning in predicting treatment response and remission in inflammatory bowel disease: a systematic review

Abstract

Background The heterogeneity of inflammatory bowel disease (IBD) and its unpredictable course
have always been a challenge for gastroenterologists, with regard to predicting the disease response
using endoscopic techniques. Machine learning (ML) models have shown some early promise in
predicting treatment response in IBD patients.

Methods We conducted a systematic review of studies investigating the application of ML to
predict treatment response and remission in IBD patients. We used the CHARMS checklist for
data extraction. Bias was assessed with the PROBAST tool.

Results We included in our review 6 studies that evaluated numbers of IBD patients ranging from
67 to 3004. ML models demonstrated low to moderate predictive accuracy for treatment response
and remission (area under the receiver operating characteristic curve: 0.489-0.811; sensitivity:
0.46-0.96; specificity: 0.56-0.98). The studies that utilized ML models with more input variables
performed better. Furthermore, only 2 studies performed external validation, and half of the
studies demonstrated a substantial risk of bias due to missing data/overfitting, and variability in
outcome definition

Conclusions ML models show considerable promise in predicting treatment outcomes and
remission in IBD. However, given the substantial bias in studies so far, future studies should use a
standardized methodology, external validation, and an interpretable broader input variable.

Keywords Machine learning, inflammatory bowel disease, treatment, monitoring, response

Ann Gastroenterol 2026; 39 (2): 247-253