Artificial intelligence for inflammatory bowel disease dysplasia detection: Current evidence and future directions

Vollständiger Abstract

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There is increasing interest in using artificial intelligence (AI) in the detection of dysplasia in patients with inflammatory bowel disease (IBD). However, the application of AI in this context is limited by dataset shift, lack of validation, and bias. The purpose of this review was to examine the role of AI in the detection of IBD-associated dysplasia through three different themes: Comparative performance, mechanisms of failure, and pathways for safe clinical application. Retrospective, prospective, and multicenter studies demonstrate that AI systems trained on non-IBD data perform poorly in inflamed colons, while IBD-specific AI models provide improved accuracy, however, still experience gaps in generalizability. Various biases like selection, annotation, device, and reporting biases which are further amplified by mismatch between training and deployment environments, undermine the reliability of AI and contribute to potential inequities in care. Practical approaches to improve the reliability and fairness of AI include multicenter IBD-focused datasets, consensus labelling, multimodal architectures, and structured post-deployment monitoring. By framing “beyond dataset shift” as an overarching concept, this review provides a framework for the development of methodologically rigorous, bias-aware AI that will support rather than undermine the prevention of colorectal cancer in IBD.