Role of artificial intelligence in irritable bowel syndrome: A narrative review

Table 4 Proposed minimum design and reporting standards for future artificial intelligence studies in irritable bowel syndrome

Domain	Minimum standard proposed	Why it matters in IBS
Intended use	State the clinical question, target population, care setting and the decision the model informs; specify whether it is diagnostic, prognostic or predictive of treatment response	Most IBS models report discrimination without stating the decision they are intended to support
Reference standard	Define how labels were assigned (Rome IV criteria with documented exclusion of organic disease), who applied them and whether assessment was blinded	IBS has no biomarker, so label uncertainty is inherited directly by any supervised model
Data elements	Pre-specify candidate features and their provenance; report missing data and how it was handled; for microbiome data report extraction, sequencing and bioinformatic pipeline	Technical factors are a dominant source of microbiome variance and differ between centres
External validation	Evaluate in at least one cohort independent of the development cohort in geography and recruitment pathway	Reported IBS models are overwhelmingly validated internally only
Temporal validation	Test stability over time and across changes in diagnostic criteria, symptom instruments and sequencing protocols	The shift from Rome III to Rome IV alters both prevalence and case mix
Calibration	Report calibration slope and intercept, or a calibration plot, alongside discrimination	Calibration determines whether a predicted probability of treatment response can be acted upon; it is almost never reported
Fairness	Report performance across sex, age, ethnicity and socioeconomic strata, and state explicitly any outcome proxy used in place of the outcome of interest	The evidence base is geographically narrow and IBS cohorts are predominantly female
Prospective impact	Before adoption, evaluate the effect on clinical decisions, resource use and patient outcomes in a prospective comparative design	No prospective impact study has been undertaken in IBS
Reporting standards	Report to TRIPOD+AI[58]; appraise risk of bias with PROBAST+AI[59]; follow DECIDE-AI[60] for early clinical evaluation	Aligns IBS work with current reporting norms for AI prediction models

IBS: Irritable bowel syndrome; AI: Artificial intelligence; TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis; PROBAST: Prediction model risk of bias assessment tool; DECIDE: Developmental and exploratory clinical investigations of decision support systems driven by artificial intelligence.