Introduction: Artificial intelligence (AI) is expected to impact all facets of inflammatory bowel disease (IBD) management, including disease assessment, treatment decisions, discovery and development of new biomarkers and therapeutics, as well as clinician-patient communication.
Areas covered: This perspective paper provides an overview of the application of AI in the clinical management of IBD through a review of the currently available AI models that could be potential tools for prognosis, shared decision-making, and precision medicine. This overview covers models that measure treatment response based on statistical or machine-learning methods, or a combination of the two. We briefly discuss a computational model that allows integration of immune/biological system knowledge with mathematical modeling and also involves a 'digital twin', which allows measurement of temporal trends in mucosal inflammatory activity for predicting treatment response. A viewpoint on AI-enabled wearables and nearables and their use to improve IBD management is also included.
Expert opinion: Although challenges regarding data quality, privacy, and security; ethical concerns; technical limitations; and regulatory barriers remain to be fully addressed, a growing body of evidence suggests a tremendous potential for integration of AI into daily clinical practice to enable precision medicine and shared decision-making.
Keywords: Artificial intelligence; Crohn’s disease; computational model; fecal calprotectin; inflammatory bowel disease; mucosal healing; precision medicine; shared decision-making.
Advances in artificial intelligence (AI) show promise for improving treatment response prediction, decision-making, and precision medicine in inflammatory bowel disease (IBD).In particular, AI could improve precision medicine for IBD by enabling identification of disease subtypes, prediction of disease progression and treatment response, selection of personalized treatments, and remote monitoring.Predictive models can benefit clinicians and patients alike by optimizing shared decision-making processes; patients can also use AI to cope with daily and long-term challenges of the disease.Beyond patients and practitioners, predictive models may positively impact healthcare structures and payers by enabling effective healthcare-resource utilization.To increase the accuracy and efficiency of AI models, biomarkers, patient-reported outcomes, and disease scores should be combined within predictive models, and the outputs should be compared with clinical trial data and real-world data for validation.