Artificial intelligence use for precision medicine in inflammatory bowel disease: a systematic review.

introductionInflammatory Bowel Disease (IBD), encompassing Crohn's disease and ulcerative colitis, presents significant clinical challenges due to its heterogeneous nature and complex etiology. Recent advancements in biomedical research have enhanced our understanding of IBD's genetic, microbial, and biochemical aspects. However, persistent issues in clinical management, including treatment non-response, surgical interventions, and diagnostic uncertainties, underscore the need for more targeted approaches. This review examines the convergence of artificial intelligence (AI) and precision medicine (PM) in IBD management. By leveraging AI's capacity to analyze complex, multi-dimensional datasets, this emerging field offers promising applications in improving diagnostic accuracy, predicting treatment responses, and forecasting disease progression, potentially transforming IBD patient care.

methodThe systematic review (SR) was conducted by searching the following databases: PubMed, PubMed PMC, BVS, Scopus, Web of Science, Embase, Cochrane, and ProQuest up to February 2024. Studies that employed AI in IBD applied to precision medicine were included.

results139 studies on applying AI in precision medicine for IBD were identified. Most studies (>70%) were published after 2020, indicating a recent surge in interest. The AI applications primarily focused on diagnosis, treatment response prediction, and prognosis. Machine learning algorithms were predominantly used, particularly random forest, logistic regression, and support vector machines. Omics data were frequently employed as predictors, especially transcriptomics and microbiome analyses. Studies demonstrated good predictive performance across all three areas, with median AUC values ranging from 0.85 to 0.90.

conclusionAI applications in IBD show promising potential to enhance clinical practice, particularly in disease prognosis and predicting treatment response. However, clinical implementation requires further validation through prospective studies. Future research should focus on standardizing protocols, defining clinically significant outcomes, and evaluating the efficacy of these tools.