Artificial Intelligence in Chronic Rhinosinusitis: From the Present to the Future

Chronic rhinosinusitis (CRS) is a common, biologically heterogeneous disease in which diagnosis, endotyping, therapy selection, and surgical decision-making all remain imperfect. Over little more than a decade, artificial intelligence (AI) applied to CRS has advanced from classical regression to machine learning, deep learning, and, most recently, computer vision and generative models, producing tools that now touch the entire care pathway. This review synthesizes that progress across four present-day capabilities and two emerging frontiers. In diagnosis, machine learning predicts probable CRS from pre-visit data while convolutional networks interpret CT, nasal endoscopy, and histopathology and infer eosinophilic endotype noninvasively. In endotyping, supervised, unsupervised, and latent-factor models, together with image-based deep learning, resolve molecularly defined subsets in CRS, repeatedly implicating IL-5 and mixed type 2/neutrophilic inflammation. In therapeutics, machine learning clarifies medical-therapy response and identifies molecular predictors of biologic benefit. In surgery, models forecast endoscopic sinus surgery success and disease recurrence, converging on asthma, radiographic sinus opacification, and eosinophilic or neutrophilic inflammation. Looking ahead, multimodal foundation models, federated learning, and computer-vision-guided intraoperative navigation promise integrated, patient-specific decision support. We also weigh the risks and limitations of AI in CRS care and outline unmet needs, including the case for international consensus on how AI is developed and evaluated. Across domains, complex algorithms do not consistently outperform parsimonious models, and external validation remains the principal barrier to clinical translation.