ChatIBD: design, safeguards, and early international use of a guideline-grounded generative AI tool for inflammatory bowel disease (IBD) professionals

Objectives To describe the design, operational safeguards, and early use of ChatIBD, a specialty-specific generative AI platform for inflammatory bowel disease (IBD), during its first 6 months of live deployment. Methods ChatIBD is an online question-answering platform that uses retrieval-augmented generation over a curated corpus of IBD guidelines. Queries undergo hybrid semantic and keyword retrieval with query expansion and reranking, and the model is instructed to answer only from retrieved material and return linked citations. Safeguards include fixed medication dosing information from European Medicines Agency (EMA), user feedback capture, and clinician review of flagged outputs. We performed a descriptive service evaluation of aggregated, de-identified platform metrics collected between 1 October 2025 and 1 April 2026. Results During the study period, ChatIBD registered 913 users and processed 7,222 messages across 3,855 conversations. Activity was recorded across 69 countries and 28 languages, with the highest message volumes from the United Kingdom (27.1%) and Spain (12.3%). Median daily message volume was 35.5 (IQR 20 to 52), and 85.1% of messages were submitted on weekdays. Medication-related queries accounted for the largest use domain, while guideline synthesis was the most frequent inferred intent. Sixteen explicit feedback events were recorded, including one negative rating that triggered clinician review and system changes. Conclusions ChatIBD showed early international uptake and repeat use as a specialty-specific, retrieval-grounded generative AI tool for IBD professionals. These findings support the feasibility of deploying a guideline-grounded clinical AI service with practical safeguards, but do not establish response accuracy, safety, or clinical effectiveness. Formal validation is in progress.