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

Table 3 Key challenges to clinical translation of artificial intelligence in irritable bowel syndrome and corresponding mitigation strategies

Challenge	Why it matters for IBS	Mitigation strategies
Data standardization	Heterogeneous diagnostic criteria, symptom instruments, and sequencing protocols limit generalizability	Rome Foundation frameworks; harmonized data-collection standards; shared microbiome pipelines
Model interpretability	“Black-box” predictions are hard for clinicians to trust or act on	Explainable AI (SHAP values, attention visualization); prefer interpretable models for high-stakes decisions
External validation	Most models are single-center; performance may not transfer across settings	Independent multi-region cohorts; temporal validation; TRIPOD+AI-compliant reporting
Privacy, consent & bias	Sensitive microbiome and psychological data; non-representative data can entrench disparities	GDPR/HIPAA-aligned governance; federated learning; fairness audits on representative datasets
Cost-effectiveness	Development and maintenance are costly; value must be demonstrated	Health-economic modeling; evidence of reduced diagnostic delay and unnecessary colonoscopy
Regulatory landscape	Adaptive and continuously learning tools face evolving oversight	FDA software-as-a-medical-device pathways; predetermined change-control plans; harmonized standards
Patient acceptance and trust	Concerns about bias, transparency, and loss of human connection shape adoption	Transparency, clinician-in-the-loop design, patient education; dedicated acceptance studies

IBS: Irritable bowel syndrome; SHAP: SHapley Additive exPlanations; AI: Artificial intelligence; FDA: Food and Drug Administration; GDPR: General Data Protection Regulation; HIPAA: Health Insurance Portability and Accountability Act; TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis.