An introduction to machine learning and generative artificial intelligence for otolaryngologists—head and neck surgeons: a narrative review

Purpose Despite 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 model...

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Veröffentlicht in:European archives of oto-rhino-laryngology Jg. 281; H. 5; S. 2723 - 2731
Hauptverfasser: Alter, Isaac L., Chan, Karly, Lechien, Jérome, Rameau, Anaïs
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.05.2024
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Head and Neck Surgery
Humans
Machine Learning
Medicine
Medicine & Public Health
Miscellaneous
Neurosurgery
Otolaryngologists
Otolaryngology
Otorhinolaryngology
Surgeons
Clinical decision support tools
Diagnostic tools
Large language models
Predictive algorithms
Machine learning
Otolaryngology
Natural language processing
Artificial intelligence
ISSN:0937-4477, 1434-4726, 1434-4726
Online-Zugang:Volltext
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Zusammenfassung:Purpose Despite 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. Methods Recent literature regarding AI principles and otolaryngologic applications of ML and LLMs was reviewed via search in PubMed and Google Scholar. Results Significant 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. Conclusions Potential 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.
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ISSN:0937-4477
1434-4726
1434-4726
DOI:10.1007/s00405-024-08512-4