ReviewOtolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery2026
Clinical Applications of Multimodal Artificial Intelligence in Otolaryngology: A State-of-the-Art Review.
Review in Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
6 authors.
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Abstract
objectiveArtificial intelligence (AI) has advanced to simultaneously process visual, auditory, and textual inputs, providing users with "multimodal" AI. Given the clinical integration potential of these tools, otolaryngologists must stay informed. This study reviews current literature on applications of multimodal AI in otolaryngology. DATA SOURCES: The MEDLINE, EMBASE, SCOPUS, Cochrane Library, Web of Science, and CINAHL databases. REVIEW
methodsDatabases were searched from the date of inception to March 4, 2025, following Preferred Reporting Items for Systematic Reviews and Meta-analyses extension for scoping reviews (PRISMA-ScR) guidelines. Studies on any application of multimodal AI in otolaryngology were included.
conclusionsForty-four studies were included, with 55% (24/44) published in 2024 and 18% (8/44) in 2025. Image and text were the most commonly combined modalities (80%, 35/44), with emerging combinations including video with vector data (2%,1/44) and omics with text and/or image (14%, 6/44). Head and neck cancer was the most common subspecialty of focus (75%, 33/44), followed by general ear, nose, and throat (ENT) (11%, 5/44). All studies applied the models for clinical education (9%, 4/44) or decision support (91%, 40/44), assessing performance in areas such as board-style examination performance (accuracy: 37%-86%) or disease classification and prognostication (area under the receiver operating characteristic curve [AUC] 0.65-0.96). However, most studies were limited to small, single-institution samples and lacked prospective validation. Model error, data set bias, and language limitations underscore the need for further refinement. IMPLICATIONS FOR PRACTICE: The application of multimodal large language models (LLMs) in otolaryngology is rapidly expanding. Clinicians must understand both the capabilities and limitations of these systems. Rigorous validation and ethical oversight will be essential to ensure the safe, equitable, and effective adoption in otolaryngologic care.
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