Artificial Intelligence in Chronic Rhinosinusitis: From Molecular Profiling to Personalized Clinical Management—A Systematic Review

Chronic rhinosinusitis (CRS) is a heterogeneous inflammatory disease imposing a substantial health and economic burden. Artificial intelligence (AI) has emerged as a promising tool to address CRS complexity in both clinical and research settings. We conducted a systematic review following PRISMA guidelines, searching PubMed, Web of Science, and Embase for peer-reviewed articles utilizing machine learning, deep learning, and natural language processing, with a focus on molecular-level or immunologic mechanisms in CRS. Thirty-one studies were included in the qualitative synthesis. Our results demonstrate that while AI-driven analysis helps stratify patient endotypes, the identified schemes exhibit substantial heterogeneity in cluster numbers and features, raising concerns about their reproducibility. AI applications also included histopathological image interpretation, drug discovery, biologic therapy selection, and prognostic modeling to evaluate revision surgery risks and olfactory dysfunction. However, using the PROBAST+AI framework, we found that 87.1% of the assessed studies carry a high overall risk of bias, mainly due to the reliance on apparent performance or internal validation and limited independent evaluation of model performance, indicating that many reported performance estimates may be optimistic. Future large-scale, multicenter collaborations integrating AI into clinical workflows will be essential to establish reliable and generalizable AI applications for personalized CRS management.