UC Davis

INTRODUCTION: Inflammatory bowel diseases (IBD) are immune-mediated conditions that are increasing in incidence and prevalence worldwide. Their assessment and monitoring are becoming increasingly important, though complex. The best disease control is achieved through tight monitoring of objective inflammatory parameters (such as serum and stool inflammatory markers), cross-sectional imaging and endoscopic assessment. Considering the complexity of the information obtained throughout a patients journey, artificial intelligence (AI) provides an ideal adjunct to existing tools to help diagnose, monitor and predict the course of disease of patients with IBD. Therefore, we propose a scoping review assessing AIs role in diagnosis, monitoring and prognostication tools in patients with IBD. We aim to detect gaps in the literature and address them in future research endeavours. METHODS AND ANALYSIS: We will search electronic databases, including Medline, Embase, Cochrane CENTRAL, CINAHL Complete, Web of Science and IEEE Xplore. Two reviewers will independently screen the abstracts and titles first and then perform the full-text review. A third reviewer will resolve any conflict. We will include both observational studies and clinical trials. Study characteristics will be extracted using a data extraction form. The extracted data will be summarised in a tabular format, following the imaging modality theme and the study outcome assessed. The results will have an accompanying narrative review. ETHICS AND DISSEMINATION: Considering the nature of the project, ethical review by an institutional review board is not required. The data will be presented at academic conferences, and the final product will be published in a peer-reviewed journal.