{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T11:57:05Z","timestamp":1791633425932,"version":"4.3.4"},"reference-count":58,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["aao-hnsfjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Otolaryngol.--head neck surg."],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To critically evaluate advances in artificial intelligence (AI) within rhinology, focusing on translational readiness, regulatory alignment, and clinical implementation pathways.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Sources<\/jats:title>\n                    <jats:p>PubMed and Scopus.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Review Methods<\/jats:title>\n                    <jats:p>PubMed and Scopus were searched using predefined Title\/Abstract Boolean strategies with publication date (January 1, 2023\u2010February 1, 2025), English\u2010language, and human\u2010study restrictions to support a structured state\u2010of\u2010the\u2010art narrative synthesis. Major application domains were categorized as automated computed tomography (CT) analysis, endoscopic computer vision, phenotyping and endotyping in chronic rhinosinusitis, outcome prediction using patient\u2010reported data, digital olfaction, and patient\u2010facing language tools. Each domain was assessed for validation status, workflow feasibility, equity, and data\u2010economics considerations, and alignment with regulatory pathways for software as a medical device.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Automated sinus CT models have achieved multi\u2010institutional external validation and appear closest to workflow translation. Endoscopic systems demonstrate promising performance in near real\u2010time video but remain largely retrospective and require evaluation in live workflows. Predictive modeling using integrated clinical, molecular, or patient\u2010reported data remains exploratory, while digital olfaction and language models lack standardized validation and regulatory oversight. Across domains, implementation barriers persist, including interoperability with electronic health records, economic disincentives to data aggregation, and risks of inequitable performance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Implications for Practice<\/jats:title>\n                    <jats:p>AI in rhinology is progressing toward integration into clinical care, with automated imaging applications leading adoption. Responsible deployment requires prospective multicenter trials, clinician\u2010supervised workflows, transparency in performance across demographic groups, and evidence of improved patient outcomes. A structured readiness framework may guide stakeholders in prioritizing regulatory\u2010feasible tools that offer measurable clinical value.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ohn.70262","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T09:13:34Z","timestamp":1777281214000},"page":"259-270","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligence in Rhinology: A State\u2010of\u2010the\u2010Art Review of Clinical Readiness and Implementation Pathways"],"prefix":"10.1002","volume":"175","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0651-6994","authenticated-orcid":false,"given":"Sholem","family":"Hack","sequence":"first","affiliation":[{"name":"City St. George's University London School of Medicine, Program Delivered by University of Nicosia at the Chaim Sheba Medical Center Ramat Gan 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