ReviewEuropean archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery2024
An introduction to machine learning and generative artificial intelligence for otolaryngologists-head and neck surgeons: a narrative review.
Review in European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Digital twins: a call for an under-developed tool in otolaryngology-head and neck surgery.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Educational Frameworks for Diagnostic Decision-Making in AI-Enhanced Head and Neck Pathology.Head and neck pathology · 2026Review
- Revolutionizing Sleep Medicine: The Impact of Machine Learning on Diagnosis, Treatment, and Personalized Care.Health science reports · 2026Article
- Comprehensive Evaluation of AI Consent Forms in Otolaryngologic Surgery.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Hybrid Reconstruction in Head and Neck Surgery: Integration of Virtual Planning, Navigation, and Robotic Microsurgery.Journal of clinical medicine · 2026Review
- Observational
- Digital health technologies in swallowing care from screening to rehabilitation: A narrative review.Auris, nasus, larynx · 2025Review
- Artificial intelligence in pediatric otolaryngology: A state-of-the-art review of opportunities and pitfalls.International journal of pediatric otorhinolaryngology · 2025Review
- Development of Machine Learning Copilot to Assist Novices in Learning Flexible Laryngoscopy.The Laryngoscope · 2025Article
- Artificial intelligence optimizes the standardized diagnosis and treatment of chronic sinusitis.Frontiers in physiology · 2025Review
- Artificial Intelligence in Audiology: A Scoping Review of Current Applications and Future Directions.Sensors (Basel, Switzerland) · 2024Article
- Transforming ENT Healthcare: Advancements and Implications of Artificial Intelligence.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Assessing the Accuracy of Generative Conversational Artificial Intelligence in Debunking Sleep Health Myths: Mixed Methods Comparative Study With Expert Analysis.JMIR formative research · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
purposeDespite the robust expansion of research surrounding artificial intelligence (AI) and machine learning (ML) and their applications to medicine, these methodologies often remain opaque and inaccessible to many otolaryngologists. Especially, with the increasing ubiquity of large-language models (LLMs), such as ChatGPT and their potential implementation in clinical practice, clinicians may benefit from a baseline understanding of some aspects of AI. In this narrative review, we seek to clarify underlying concepts, illustrate applications to otolaryngology, and highlight future directions and limitations of these tools.
methodsRecent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar.
resultsSignificant recent strides have been made in otolaryngology research utilizing AI and ML, across all subspecialties, including neurotology, head and neck oncology, laryngology, rhinology, and sleep surgery. Potential applications suggested by recent publications include screening and diagnosis, predictive tools, clinical decision support, and clinical workflow improvement via LLMs. Ongoing concerns regarding AI in medicine include ethical concerns around bias and data sharing, as well as the "black box" problem and limitations in explainability.
conclusionsPotential implementations of AI in otolaryngology are rapidly expanding. While implementation in clinical practice remains theoretical for most of these tools, their potential power to influence the practice of otolaryngology is substantial. LEVEL OF EVIDENCE: 4:
Indexed as
Identifiers
38393353What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.