Beyond the Hype: Understanding the Limits, Errors and Risk Areas of Artificial Intelligence in Gastroenterology

Abstract

Artificial intelligence (AI) has rapidly expanded across gastroenterology, enabling advances in real-time endoscopic detection, radiologic interpretation, digital pathology, multimodal prognostication, and electronic health record-based decision support. Despite strong performance in controlled studies and increasing regulatory adoption, the clinical integration of AI remains challenged by limitations that threaten reliability, safety, and equitable deployment. This editorial synthesizes the major sources of vulnerability across current AI applications in gastroenterology, including dataset bias, limited generalizability, annotation variability, underrepresentation of rare lesions, and performance degradation in real-world environments. Endoscopic AI systems-the most mature applications-face persistent false positives, false negatives, alert fatigue, and operator deskilling, while most algorithms lack explainability and fail to incorporate essential clinical context. Predictive models based on EHR or imaging data are hindered by data noise, evolving clinical practices, and susceptibility to model drift. Additional risks arise from automation bias, suboptimal workflow integration, and unresolved ethical, regulatory, and liability considerations. Ensuring safe and meaningful clinical deployment requires continuous post-deployment monitoring, rigorous external validation, improved interpretability, and the development of multimodal systems that integrate imaging with clinical and biological data. Equally critical is clinician education to preserve human oversight and prevent overreliance on algorithmic output. By recognizing and addressing these limitations, the field can move beyond accuracy-focused evaluation toward designing AI systems that are robust, transparent, and capable of improving patient-centered outcomes in diverse real-world settings.

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